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THE CO-EVOLUTION OF GALAXIES AND THEIR SURROUNDING ENVIRONMENTS IN MASSIVE GALAXY CLUSTERS

A DISSERTATION SUBMITTED TO THE DEPARTMENT OF PHYSICS AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Steven Ehlert August 2013

© 2013 by Steven Robert Ehlert. All Rights Reserved. Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/rc742pg9006

ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Steven Allen, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Tom Abel

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Stefan Funk

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Risa Wechsler

Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost for Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.

iii °c Copyright by Steven Ehlert 2013 All Rights Reserved

ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

(Steven W. Allen) Principal Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

(Risa Wechsler)

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

(Tom Abel)

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

(Stefan Funk)

Approved for the University Committee on Graduate Studies.

iii iv Abstract

A key yet poorly understood component to galaxy evolution models is the influence of the local environment, and is a particularly important aspect to understand the evolution of galaxies in galaxy clusters. Galaxy clusters are not only host to significant overdensities of galaxies, but these galaxies are embedded in a hot, diffuse Intracluster Medium (ICM). The ICM has long been observed to have important and lasting impacts on the properties of their host galaxies, which are commonly attributed to a number of unique astrophysical processes such as the stripping of galaxy gas by the ICM due to ram pressure and repeated tidal interactions. The galaxies in turn have also been observed to play an important role in the evolution of the ICM, especially near the centers of galaxy clusters where powerful outbursts from Active Galactic Nuclei (AGN) can provide a quasi-steady source of heating to the surrounding ICM, a process known as AGN feedback. Although great progress has been made in recent years to understand the connection between galaxies and the ICM, the precise influences of the ICM on cluster galaxies and the astrophysical processes that drive galaxy evolution in clusters are still subject to important uncertainties. It is clear from the data, however, that the predictions of the simplest models of ram pressure stripping, tidal encounters, and AGN feedback are inconsistent with observations; in particular observations taken with the modern generation of X-ray telescopes such as Chandra and XMM-Newton. These telescopes, with their superb angular resolution, large collecting areas, and wide fields of view, have revolutionized our understanding of galaxies in clusters, in particular the population of AGN in galaxy clusters. In this thesis, I will present a series of results regarding the nature of ram pressure stripping, harassment, and AGN feedback in cluster member galaxies driven primarily by X-ray observations. These results include multiwavelength observations of the one of the most extreme cases of AGN feedback currently known, a mosaic of galaxies in the nearby Virgo Cluster which are all undergoing different variants of ram pressure stripping, and the first set of results from one of the largest X-ray AGN surveys ever undertaken with Chandra. All of these results suggest an intricate choreography between the gas reservoirs intially hosted by galaxies and the surrounding ICM, with tests that investigate length scales ranging from 10 kpc to 1 Mpc. ∼ ∼

v vi Preface

This thesis discusses observational studies that investigate in detail the unique mechanisms by which galaxies in galaxy clusters interact with one another and the surrounding Intracluster Medium (ICM). All of these processes inﬂuence not only the properties of galaxies hosted in clusters but also the thermodynamic and chemical properties of the ICM. We use the introduction to discuss the physics of these processes, the properties of galaxy clusters in general, and the observational signatures of these proceses on both the galaxy population and the ICM. Additionally we discuss some of the general characteristics of Active Galactic Nuclei (AGN), a special class of galaxy whose presence in galaxy clusters drives the majority of this work. Chapter 2 focuses on a remarkable example of central AGN feedback and its impact on the ICM, MACS J1931.8-2634, which was published in Monthly Notices of Royal Astronomial Society (MNRAS) in 2011. Chapter 3 emphasizes the prevalence of ram pressure stripping in cluster member galaxies in the Virgo Cluster using XMM-Newton data proposed for and acquired during my thesis, and published in MNRAS in 2013. Chapters 4-6 discuss a new large survey of X-ray selected AGN undertaken in a sample of 135 galaxy clusters, of which 43 also have deep optical imaging data. At the time of this thesis, one paper utilizing this analysis was published in MNRAS in 2013, one paper has been submitted for publication, and a third is in the ﬁnal stages of preparation before submission. Chapter 7 discusses these results and their implications to our understanding of galaxies and galaxy clusters, and in Chapter 8 we discuss future prospects and projects underway that will shed new light on these processes.

vii viii Acknowledgements

To paraphrase a quote from John Donne, “No Ph.D thesis is an island”, and certainly this PhD thesis is no exception. It builds upon the works of other astronomers and physicists too numerous to count, but there are a few people whose contributions have been so invaluable to this work that I must express my gratitude here. Firstly, I cannot give enough thanks to my adviser, Professor Steve Allen, for the time, encour- agement, and effort that he has invested into my work over the years. His insight and intuition into all of these projects and countless other ideas not discussed here have been essential to my develop- ment as an astrophysicsist over the course of the past ﬁve years. It is no exaggeration to state that this thesis work simply would not be possible without his faith and support in me and the research I was doing. Along that same line, I must also express my thanks to the graduate students, postdocs, and staff members of Prof. Allen’s group that have made my stay at Stanford so enjoyable: Glenn Morris, Anja von der Linden, Norbert Werner, Aurora Simionescu, David Rappetti, Tim Schrabback, Stefan Hilbert, Julie Hlavacek-Larrondo, Rebecca Canning, Irina Zhuravleva, Adam Mantz, Evan Million, Doug Applegate, Partick Kelly, Ondrej Urban, Yuanyuan Su, and the recently arrived Adam Wright. I owe you all my deepest gratitude for your contributions not only to my papers and research but also to the social life of the XOC group. It has been a wonderful experience being a part of this group, and I hope it continues to be a wonderful experience for new students and postdocs for years to come. In addition to the members of XOC listed above, I must also express my gratitude to the rest of my co-authors on these papers: Greg Taylor, Gianfranco Gentile, Harald Ebeling, Mark Allen, Andy Fabian, Jeremy Sanders, Robert Dunn, Robert Schmidt, Niel Brandt, Yongquan Xue, Bin Luo, Jeffrey Kenney, and Alexis Finoguenov. I also need to thank all of the faculty, staff, students, and administrators of the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), whose general enthusiasm and support of astrophysics have made coming into my ofﬁce a joy nearly every day. In particular, I also wish to thank my thesis committee of Tom Abel, Risa Wechsler, Stefan Funk, and Nick Melosh for their insight and suggestions on this work as well as their patience in scheduling the defense.

ix On a more personal level, I also wish to thank my parents, brother, sister, and roommate Dan for tolerating my behavior near proposal deadlines, postdoc job applications, and qualifying exams. Your patience with me on those anxious days does not go unappreciated. Last but not least, I wish to thank my lovely new wife Julie for all of her love and support throughout the process of writing this thesis. Without her, the bulk of my time at Stanford would have undoubtedly been far more lonely and difﬁcult. She has been a tremendous source of support and inspiration over the years, and has taught me so much that I didn’t know (including many things I didn’t know I didn’t know). Although I have always considered myself fortunate, I have known for years now that I am the luckiest man on Earth to have found her.

x Contents

Abstract v

Preface vii

Acknowledgements ix

1 Introduction 1 1.1 Introduction to Galaxy Clusters ...... 2 1.2 Astrophysical Processes in Clusters of Galaxies ...... 2 1.2.1 Dark Matter ...... 2 1.2.2 Galaxies ...... 3 1.2.3 The Intracluster Medium ...... 5 1.2.4 Determinations of Cluster Masses ...... 10 1.2.5 Testing Cosmological Models with Galaxy Clusters ...... 12 1.3 Observations of Active Galactic Nuclei ...... 13 1.3.1 Taxonomy and Accretion Modes of AGN ...... 13 1.3.2 X-ray Emission of Active Galactic Nuclei ...... 14 1.4 Galaxy Evolution in Clusters ...... 15 1.4.1 Ram Pressure Stripping ...... 16 1.4.2 Harassment ...... 18 1.4.3 Central AGN Feedback ...... 19 1.5 Evolution in AGN in the Field ...... 20 1.5.1 Connecting X-ray and Optical AGN ...... 23 1.6 Star Formation and AGN in Cluster Galaxies ...... 23 1.7 Key Tests in this Study ...... 26

2 An Extreme Example of Central AGN Feedback in MACS J1931.8-2634 39 2.1 Introduction ...... 39

xi 2.2 Chandra Data Reduction and Processing ...... 39 2.3 X-ray Imaging Analysis ...... 40 2.3.1 Surface Brightness Proﬁles ...... 40 2.3.2 Background Subtracted, Flat-Fielded Image ...... 41 2.3.3 Substructure Analysis ...... 42 2.4 Image Deprojection Analysis ...... 44 2.5 Spectral Analysis ...... 45 2.5.1 Methods ...... 45 2.5.2 Spectral Results ...... 48 2.6 Optical structure of the cluster core ...... 53 2.7 Radio Observations with VLA ...... 57 2.8 The Central AGN: Power and Accretion ...... 58 2.8.1 Estimating the Radiative Power ...... 58 2.8.2 Estimating the Jet Power ...... 58

3 The Ubiquity of Galaxy Stripping by Ram Pressure in the Neighborhood of M86 69 3.1 Introduction ...... 69 3.2 Data Processing and Analysis Methods ...... 70 3.2.1 Imaging Analysis ...... 70 3.2.2 Thermodynamic Mapping ...... 72 3.3 Results ...... 75 3.3.1 Temperature Structure ...... 75 3.3.2 Metallicity Structure ...... 76 3.3.3 Ultraviolet Observations with GALEX ...... 77

4 The Population of X-ray AGN in Galaxy Clusters: X-ray Point Source Catalog Pro- duction 81 4.1 Introduction ...... 81 4.2 The Cluster Sample ...... 82 4.3 Initial X-Ray Data Processing ...... 82 4.4 Candidate Source Catalogs ...... 83 4.5 Follow-Up Analysis ...... 89 4.6 Background and Sensitivity Calculation ...... 94 4.6.1 Background Map Creation ...... 94

xii 4.6.2 Deriving the Sensitivity Maps ...... 95 4.7 The Final X-ray AGN Survey ...... 96

5 The Population of X-ray AGN in Galaxy Clusters: Results from the X-ray Data 107 5.1 Introduction ...... 107 5.1.1 Cumulative Number Counts ...... 109 5.1.2 The Radial Distribution of X-ray Sources ...... 109 5.2 The Luminosity of Cluster Member AGN ...... 111 5.3 Evolution of Cluster AGN Beyond Self-Similarity ...... 114

6 The Population of X-ray AGN in Galaxy Clusters: The Fraction of Galaxies Hosting X-ray AGN 119 6.1 Introduction ...... 119 6.1.1 Additional Limitations ...... 119 6.1.2 Optical Catalog Production ...... 120 6.1.3 COSMOS as a Control Field ...... 121 6.1.4 Counterpart Matching ...... 121 6.1.5 Luminosity and Stellar Mass Limits ...... 122 6.2 Results ...... 123 6.2.1 The Projected Density of Cluster X-ray Sources ...... 123 6.2.2 The Projected Density of Cluster Optical Sources ...... 124 6.2.3 The Fraction of Cluster Galaxies Hosting X-ray AGN ...... 124 6.2.4 The Fraction of BCG’s Hosting AGN ...... 126

7 AGN Feedback and Ram Pressure Stripping: The Physical Picture 129 7.1 Introduction ...... 129 7.2 Extreme Heating by Central AGN Feedback in MACS J1931.8-2634 ...... 129 7.3 The Nature of Ram Pressure Stripping in Cluster Galaxies in the Vicinity of M86 . 132 7.3.1 Correlation of the Coolest X-ray Gas and Hα Filaments ...... 133 7.3.2 On The Origin of the Hα Filaments ...... 134 7.3.3 Combining AGN Feedback with Ram Pressure Stripping ...... 135 7.3.4 On the Frequency of Stripping in Cluster Member Galaxies ...... 136 7.4 The Inﬂuences of the ICM on AGN ...... 137 7.4.1 Triggering AGN by Harassment ...... 137

xiii 7.4.2 The Fraction of Galaxies Hosting X-ray AGN ...... 138 7.4.3 Evolution Beyond Self-Similarity ...... 139

8 Unsolved Questions and Near Term Prospects for Galaxies in Clusters 141 8.1 Introduction ...... 141 8.2 Statistical Background Subtraction of Field Galaxies ...... 141 8.3 Spectroscopically Conﬁrmed Cluster Member AGN ...... 143 8.3.1 X-ray Spectra of Cluster Member AGN ...... 143 8.3.2 The Host Galaxies of Cluster Member AGN ...... 143 8.4 Correlating X-ray and Radio AGN ...... 144 8.5 Larger Samples at Higher Redshift and Lower Mass ...... 144

xiv List of Tables

2.1 Summary of the two Chandra observations of MACS J1931.8-2634. Exposure times are the net exposure after all cleaning and processing as described in Sec- tion 2.2...... 40 2.2 Spectroscopic measurements of the X-ray bright ridges to the north and south of the central AGN. Temperatures are given in keV, metallicities are in solar units, and the 1 cooling flow rate is given in M yr− . Upper limits are given at the 90% confidence ⊙ level...... 52 2.3 Summary of the optical data. All images were taken with SuprimeCam at the Subaru telescope. The quoted seeing values are those of the coadded images...... 53 2.4 Spectral models for non-thermal emission of the central AGN. The three models are described in Section 2.8.1. The absorption column densities are given in units 22 2 of 10 cm− and all fluxes and luminosities are given in the energy range of 0.7- 8.0 keVand take into account their respective absorption. The units for the flux 13 2 1 43 1 are 10− erg cm− s− , and the units for luminosity are 10 erg s− . Errors listed are 1σ confidence levels. The final column provides the C-statistic and degrees of freedom for the best fit model...... 60 2.5 Calculations of the enthalpy and jet power in MACS J1931.8-2634 for the cavities shown in Fig. 2.15. These calculations follow the procedure described in Section 2.8.2 and are also discussed in Allen et al. (2006). The first seven rows are the prior assumptions going into the calculation while the final six rows are derived quantities. 61

3.1 Summary of the ﬁve XMM-Newton observations utilized in this study. Exposure times are the net exposure after all cleaning and processing as described in Section 3.2 for each detector. The M84 observation (Obs # 0673310101, denoted by an asterisk) was heavily contaminated by ﬂares. The original event list was split into two separate event lists for each MOS instrument, the summed exposure time of which is shown below...... 72

xv 4.1 Summary of the cluster sample and Chandra observations listed in order of cluster redshift...... 84 4.1 Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift...... 85 4.1 Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift...... 86 4.1 Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift...... 87 4.1 Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift...... 88 4.2 Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize a mix of ACIS-S × − − − and ACIS-I observations...... 97 4.2 Continued ...... 99 4.2 Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize a mix of ACIS-S × − − − and ACIS-I observations...... 100 4.2 Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize a mix of ACIS-S × − − − and ACIS-I observations...... 101

xvi 4.2 Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize a mix of ACIS-S × − − − and ACIS-I observations...... 102

5.1 Flux limits for the radial profiles and the expectations for the X-ray point source density from CDFS and COSMOS deep fields in all three energy bands. The columns list: (1) the energy band; (2) the flux limit used in constructing the radial profile fits, 2 1 in units of erg cm− s− ; (3) the density of field sources from the CDFS at that flux limit; (4) the density of field sources from COSMOS at that flux limit; (5) the

measured background density between 3 and 5 r500 from the radial proﬁle; (6) the

number of sources detected within 2r500 at that ﬂux limit, across all clusters; (7) 2 the survey area within 2r500 at that ﬂux limit, in units of deg− ; (8) the average ex-

cess number of sources per cluster above that flux limit within 2r500, determined by extrapolating measurements of the field density from the best-fit constant model

between 3 and 5 r500; and (9) the best-ﬁt power-law index for the projected source density of cluster member AGN...... 110

xvii 5.2 Results of our MCMC analysis to determine the posterior probability distributions of evolutionary parameters in our projected source density profiles. Top: Input priors on the XLF after converting published results to our energy band. All of the priors with error bars are assumed to be normally distributed, while those without error bars were assumed to be fixed. Our priors have error bars a factor of 2 larger than the published values in order to account for any potential systematics that may arise in the energy band conversion. Bottom: The resulting parameter values from our posterior probability distributions for terms beyond self-similarity. For each parameter, we show the median value and its 68% confidence interval, as determined by the 1-dimensional posterior probability distributions. All posterior distributions were determined simultaneously using an MCMC analysis that either accounts for the normalization and slope terms separately (the normalization and spatial models, respectively), or in one that accounts for all four parameters simultaneously (the full model). We find that one parameter shows statistically significant deviations from our self-similar prediction: ζ, the scaling of the overdensity with mass...... 117

xviii List of Figures

1.1 A simulated galaxy cluster, from the Millennium Simulation (Springel et al., 2005). This cosmological simulation utilized 21603 dark matter particles over a co-moving 1 volume of 500 Mpc h− . The majority of the structures have collapsed into large scale filaments. At the intersection of these filaments reside galaxy clusters, the initial sites of the largest perturbations of the cosmic density field, as shown as the center of this figure. Galaxy clusters are continuously accreting new galaxies and ambient gas from the surrounding medium which continues to virialize. Figure accredited to the Max-Planck-Institute for Astrophysics in Garching, Germany. . . 3

1.2 The red sequence of cluster member galaxies identified using color magnitude di- 1 agrams for all sources located within 2h− Mpc surrounding two separate galaxy clusters, from Koester et al. (2007). These diagrams are derived using SDSS photometry in the g,r, and i bands. The largest plus signs indicate the BCG’s while the smaller plus signs denote galaxies identified as cluster members Top: Color- magnitude diagram for Abell 2142, which is at a redshift of z = 0.0942. Bottom: Color-magnitude diagram for the more distant cluster Abell 1682 (z = 0.23). In this higher redshift cluster, the red sequence has moved rightward (fainter fluxes) and upward (redder colors) due to cosmic expansion...... 27

1.3 The morphology-density relationship, as determined by Goto et al. (2003), which shows the different galaxy populations as function of environment density. The red dashed line denotes the fraction of galaxies that are classiﬁed as early-type, i.e. elliptical galaxies. The blue dashed line denotes the fraction of galaxies that are late-type, i.e. spiral galaxies. The green solid line and teal dotted line denote the fractions of galaxies identiﬁed as intermediate-type and early-type disk galaxies, respectively. It is clear from these data that spiral galaxies are preferentially found in sparse environments while elliptical galaxies are observed at higher rates in dense environments like galaxy clusters...... 28

xix 1.4 Theoretical spectral models for the Intracluster Medium calculated using the MEKAL model (Kaastra & Mewe,

1993) in XSPEC (Arnaud, 2004), which calculates the emission from a hot, single temperature, optically thin

plasma with metals. We use the photoelectric absorption model of Balucinska-Church & McCammon (1992), and

the solar metallicity abundance table of Anders & Grevesse (1989). All of these spectra are shown in the rest frame

(z = 0) and have the same emission measure. Each sub-ﬁgure shows how the emission spectrum changes based

on variations of a single parameter. a) Emission spectra models where the temperature is varied from 0.5 keV (red

curve) to 3.0 keV (blue curve). It is clear from these models that higher temperature emission not only results in

broader continuum observable to higher energies, but the relative emission of different iron lines varies dramatically

with temperature. The bump near 1 keV in the kBT = 0.5 keV model is due to Fe-L emission lines. b) Emission ∼ spectra models where the overall metallicity is varied from 0.1Z (red curve) to 1.0Z (blue curve). Varying the ⊙ ⊙ metallicity at this temperature results in varying prominence of the Fe-L complex lines (at energies near 1 keV) ∼ and the Fe-K line (at an energy of 6.4 keV). c) Emission spectra where the intervening Galactic absorption column

density is varied from 2 1020 cm 2 (red curve) to 1 1021 cm 2 (blue curve). The exponential cutoff of observed × − × − photons below 1 keV is clearly shown, and the precise cutoff energy increases with absorption column depth. . . 29

1.5 Comparisons of the 2-dimensional surface brightness distributions of two galaxy clusters: a cool-core cluster (Abell 2029, left) and non cool-core (Abell 2319, right), from Million & Allen (2009). Both clusters have been re-scaled to account for their different redshifts. Cool core clusters (such as Abell 2029) have very pronounced surface peaks, while non cool-core clusters do not. The centers of cool core clusters typically have high densities of ICM ( 0.1 cm 3), which enable it to cool rapidly ∼ − (. 1 Gyr). Since the surface brightness scales as n2 and undergoes a cooling ∼ e ﬂow that increases its density further, these regions are dramatically brighter than the surrounding regions which do not have sufﬁcient time to cool...... 30

1.6 The differences between scaled temperature profiles of cool-core and non cool-core clusters from Sanderson, Ponman & O’Sullivan (2006). This profiles shows the average temperature profile of galaxy clusters after dividing by their mean temper-

atures (measured in the radial range of 0.1 0.2r500). It is clear that while non-cool − core clusters are broadly consistent with a single temperature across their radial range, cool core clusters show a marked drop in their central regions...... 31

xx 1.7 The small scale inﬂuences of ram pressure stripping in the Virgo Cluster spiral galaxy NGC 4402, from Crowl et al. (2005). The bowed shape of the galaxy disk, combined with the north-south asymmetry in the dust (the red and yellow emission to north of the blue disk) suggest that galaxy gas is being stripped by and interspersed into the Virgo ICM by ram pressure. Reproduced with permission from the National Optical Astronomy Observatory/Association of Universities for Research in Astronomy/National Science Foundation. Copyright WIYN Consortium, Inc., all rights reserved...... 32

1.8 Heating by central AGN feedback in the Perseus Cluster, the quintessential cool core galaxy cluster (Fabian et al., 2006). The bright central point source is NGC 1275, the BCG for the Perseus Cluster. The deficits in surface brightness observed to the south and northwest of NGC 1275 are the result of central AGN feedback. More specifically, NGC 1275 is injecting large amounts of energy into the ICM by way of jets, which inflate large cavities in the surrounding ICM. The “inflation” of these cavities results in large amounts of heating in the form of sound waves (observed as ripples in the image) and shock fronts. Reproduced with permission from NASA/CXC/IoA/Andrew Fabian ...... 33

1.9 The most powerful radio-mode AGN outburst yet observed, in the galaxy cluster MS 0735.6+7421, from McNamara et al. (2005). The blue denotes the locations of X-ray emission as detected by Chandra, while the pink denotes radio emission as detected by the Very Large Array. It is clear from this image that the radio emission resides in “cavities” in the X-ray emitting ICM. Assuming that these cavities are inﬂated by the kinetic energy of the radio-bright jets ejected by the AGN, the AGN jet power has been estimated at 1046 erg s 1 , roughly 3 orders of magnitude higher ∼ − than the X-ray luminosity of the central AGN. Reproduced with permission from NASA/CXC/SAO/Brian McNamara ...... 34

xxi 1.10 The cumulative number counts (logN logS ) of X-ray point sources in the CDFS, − as determined in Lehmer et al. (2012) in four energy bands. The blue, red, and green data points denote the contributions to the total cumulative number counts (in black) from AGN, normal galaxies, and Galactic stars, respectively. In the bottom inset panels for each sub-figure is the fractional contribution of each source class to the total number counts at each flux limit. At most flux limits feasible to a typical Chandra observation (& 10 15 erg cm 2 s 1 in the 0.5 8.0 keV band), the vast − − − − majority of the X-ray point sources are AGN...... 35

1.11 The best ﬁt LDDE model for the XLF from Ueda et al. (2003), which shows the differential comoving number density of AGN at given luminosity, binned by redshift. There is compelling evidence from these curves that the break luminosity and overall normalization both vary with redshift, which suggests that an LDDE model is the appropriate ﬁt to the data...... 36

1.12 The comoving spatial density of luminous AGN as a function of redshift. The model curves were determined by integrating the LDDE XLF model from Ueda et al. (2003) for the three luminosity ranges shown. This ﬁgure shows that the comoving density of the most luminous AGN peaked at z 2, while density of less ∼ luminous AGN peaks at lower redshifts. For this reason, AGN are inferred to form in an “anti-hierarchical” fashion...... 37

2.1 Combined X-ray surface brightness proﬁle for MACS J1931.8-2634 in the energy range 0.7-2.0 keV. The red points denote the total surface brightness, while the black points denote the background subtracted surface brightness...... 41

2.2 Combined, background subtracted, ﬂat-ﬁelded image of MACS J1931.8-2634 in the energy range of 0.7-2.0 keV. a) The central 5.7 5.7 of MACS J1931.8-2634. b) ′ × ′ Same as in (a), but zoomed in by a factor of about 2 and overlaid with logarithmic surface brightness contours in blue. The centroids of these contours shift north and south of the central AGN by distances with an amplitude of up to 20-30 kpc. c) ∼ Same as in (a) but zoomed in by a factor of about 8 to focus on the central AGN, the bright ridges about 25 kpc to the north and south, and the diffuse emission extending further to the south...... 43

xxii 2.3 Images of MACS J1931.8-2634 that emphasize substructure. a) Image of MACS J1931.8-2634 after applying a high-frequency bandpass ﬁlter. This image better shows the central AGN and bright ridges to the north and south. Depressions in the X-ray emission also arise to the east and west of the central AGN. b) Image of MACS J1931.8-2634 after subtracting the best-ﬁt elliptical-β continuum model and adaptively smoothing the residuals. This image better shows a large scale ( 200 ∼ kpc) spiral feature wrapping around the center of the cluster...... 44

2.4 Best fit profile from the image deprojection analysis. a) The best fit integrated gravitational mass profile in black, with the 1σ confidence interval shown as the red curves. b) The integrated bolometric luminosity profile. c) The cooling time profile. d) The equivalent mass deposition rate profile. The high luminosity within the central 50 kpc leads to very short cooling times in this region and very large nominal mass deposition rates. For comparative purposes, the nominal mass deposition rate 1 in the Perseus Cluster is roughly 400 M yr− (Fabian et al., 2002)...... 46 ∼ ⊙

2.5 Thermodynamic maps for MACS J1931.8-2634. a) The surface brightness image of Fig. 2.2(a) with the ﬁeld of view of the thermodynamic maps overlaid in blue. b) 3 Temperature, kT, in units of keV. c) Pressure, in units of keV cm− . d) Entropy, in units of keV cm2. The 1σ fractional uncertainties in the mapped quantities are < 20 ∼ per cent. The white region at the center of these maps is the exclusion region for the central AGN. There are a total of 60 independent regions shown in these maps, which have a ﬁeld of view approximately 200 arcsec (1 Mpc) in diameter. . . . . 50

2.6 The thermodynamic structure around the central AGN. These maps are identical to those shown in Fig. 2.5, but zoomed in on the central AGN by approximately a factor of 4. a) Temperature, in keV. b) Entropy, in units of keV cm2. It is clear in both of these maps that the lowest temperature and entropy gas is located approximately 25 kpc to the north of the central AGN...... 51

2.7 The regions deﬁned as the bright northern and southern ridges, drawn over the image of Fig. 2.2(c) in blue. These encapsulate the regions of the brightest X-ray emission in the cluster apart from the central AGN. The spectral properties of these regions are discussed in detail in Section 2.5.2...... 54

xxiii 2.8 The azimuthally averaged temperature profile of MACS J1931.8-2634. The red triangles denote the projected temperature profile, while the black squares denote the deprojected profile. The central region has a much higher temperature than the adjacent regions, which suggests that shock heating might be present. The radius of the temperature minimum corresponds to the distance of the northern and southern ridges...... 55

2.9 Azimuthally averaged thermodynamic profiles for MACS J1931.8-2634 with the bright northern and southern ridges excluded. a) Projected (red triangles) and deprojected (black squares) temperature profiles. b) Deprojected density profile. c) Deprojected pressure profile. d) Deprojected entropy profile. There are clear discontinuities in both the temperature and density profiles at distances of r 70 kpc ∼ and further discontinuities in the density profile, the origin of which may be either bulk motion of cold fronts or weak shock heating...... 62

2.10 Azimuthally averaged metallicity proﬁles of MACS J1931.8-2634. The black curve is the mean proﬁle derived for the Low Entropy Core (LEC) sample from Leccardi,

Rossetti & Molendi (2010) scaled to the estimated r180 for MACS J1931.8-2634. The blue circles are the projected metallicity proﬁle, while the red triangles are the deprojected metallicity proﬁle...... 63

2.11 (a): BRz image of the central 2.6 2.6 (780kpc 780kpc) of MACS J1931.8-2634. ′ × ′ × The sightline to MACS J1931.8-2634 is close to the galactic center, and thus most objects in this image are foreground stars. The box indicates the ﬁeld of Fig. 2.12.

(b): The deepest band, R, smoothed with a 0′′. 6 Gaussian kernel, and scaled to bring out low-surface brightness features. The black contour follows a surface brightness 2 level of 28.3 mag arcsec− . Bright stars are marked as white circles. For the two stars north of the BCG, red circles indicate where the PSF surface brightness reaches 28.3 mag arcsec 2. Note how the BCG envelope and the intra-cluster light are ∼ − highly elongated in the N-S direction...... 64

xxiv 2.12 Optical structure of the BCG of MACS J1931.8-2634. (a): SuprimeCam BRz image of the central 30 arcsec 30 arcsec. (b): For this image, the contribution from the × old stellar population of the BCG (as traced by the SuprimeCam I-band image) was subtracted from each of the B,R and z images before combining them to a color image. This enhances the blue and pink features visible to the southeast and northwest of the central AGN. “Pink” signals contributions from predominantly the blue (B) and the red (z) channel. At the redshift of the cluster, the Hα line falls into the z-band, and thus this emission likely stems from Hα nebulosity surrounding MACS J1931.8-2634. The blue emission, on the other hand, likely signals a young stellar population. Interestingly, the Hα emission and young stars coincide in the northwestern region, whereas in the southeast Hα emission is absent, or significantly weaker. (c): Contours of the X-ray surface brightness map overlaid on the image in (b). The brightest knots in the northwestern filament coincide with the peak of the cluster X-ray emission north of the AGN point source. Thermodynamic mapping shows that this is also the coolest, densest part of the ICM, i.e. the cool core. A second peak (which also corresponds to cold, dense gas) is also seen close to the southwestern filament. The X-ray cavities, on the other hand, are located at 90deg ∼ angles to the bright filaments. (d): Overlay of the radio emission on (b). (e): The HST WFPC2 snapshot, showing the same field of view as a). (f): The central 7.5 arcsec 7.5 arcsec of the HST snapshot. Note the bright knots in a spiral-like × structure emanating to the Northwest of the central, brightest knot...... 65

2.13 1.4 GHz radio emission in MACS J1931.8-2634 observed with the VLA. a) X-ray image from Fig. 2.2(b) overlaid with radio contours in magenta. The NAT galaxy to south of the center of the cluster is the source of the brightest radio emission in this cluster. b) X-ray image from Fig. 2.2(c) with the radio contours overlaid in magenta. This figure shows the amorphous structure of the central radio source, which is clearly centered on the X-ray bright AGN. The radio contours are logarithmically spaced between 9 10 5 and 0.11Jy/beam. The beam size for this observation is × − 1.25 arcsec 2.78 arcsec, and the position angle of the beam ellipse is 5.34 degrees. 66 × xxv 2.14 Determining the radiative power emitted from the central AGN. a) The region of spectral extraction for the AGN (in blue) and background (magenta). b) The spectrum of the AGN with the best-fit power-law model including a fixed Galactic absorption and a free intrinsic absorption. The residuals of the fit are shown in the panel below...... 67

2.15 Estimating the jet power using the X-ray cavities and radio emission. The image of Fig. 2.3(a) is shown with the radio contours overlaid in magenta, the radio cavities overlaid in blue, and the minimal X-ray cavities overlaid in black. The determination of the 4PV enthalpy and jet power are derived from a Monte Carlo analysis that generates plausible elliptical cavities based on these ellipses. The true cavity volumes are expected to reside between the volumes calculated from these two sets of cavities...... 67

3.1 Background-subtracted, exposure corrected mosaic of ﬁve XMM-Newton pointings 1 surrounding M86 (in units of cts s− ), utilizing the MOS+PN detectors in the energy band of 0.3 1.0 keV. The four X-ray brightest Virgo Cluster members observed − in this mosaic (M86, M84, NGC4388, and NGC 4438) are labeled. Diffuse gas is observed trailing to the northwest of M86, to the east of M86 in the direction of NGC 4438, to the south of M84, and to the east of NGC 4388...... 71

3.2 An r-band image of the region surrounding M86 from the Sloan Digital Sky Sur- vey. The X-ray surface brightness contours are overlaid in white, which show the stripped X-ray emitting gas trailing these galaxies. The stripped gas tail is especially pronounced for the M86 at the center of the image. Tails of stripped gas are observed to the south of M84 and to the northwest of M86. Diffuse soft emission is detected trailing eastward from M86 in the direction of NGC 4438. Faint, diffuse emission is also detected at the position of the edge-on spiral galaxy NGC 4402, located 10 to the north of M86...... 73 ∼ ′ xxvi 3.3 Temperature and metallicity maps of the 1 region surrounding M86, including ∼ ◦ the galaxies M84, NGC 4388, and NGC 4438. The upper row shows our low S/N ( ∼ 4000 counts per bin) temperature and metallicity maps. The values show our best- fit parameters using a single-temperature thermal emission model. The lower row shows temperature and metallicity maps from our high S/N spectra ( 7000 counts ∼ per bin). The values in these maps are derived from a two-temperature fit, with the second temperature fixed to 2.3 keV and the metallicities of both components tied to the same value. This two-temperature fit optimally identifies regions hosting gas significantly cooler than the ambient Virgo Cluster gas, which has an assumed temperature of 2.3 keV. The inclusion of an additional temperature component also reduces metallicity measurement biases...... 78

3.4 A zoomed in view of M84 with XMM-Newton. Both of these images show the same field of view. The dashed ellipse denotes the region which we use to estimate the rate at which X-ray halo gas is being stripped from M84. Left: The background subtracted, exposure corrected image of M84 in the 0.3 1.0 keV energy band. The − black contours are derived from Chandra observations of this galaxy, which show the extent of the AGN-driven cavities (Finoguenov et al., 2008). Right: The corresponding temperature map of this same region, in units of keV. The bin map is similar to that described in §3.2.2, although with a only 250 net counts per bin ∼ in order to resolve smaller scale features. Each bin is fit with a single-temperature plasma model with Galactic absorption (PHABS APEC). The same Chandra con- × tours are overlaid in black. The brighter gas trailing to the south of M84 is shown to be significantly cooler than the ambient ICM...... 79

3.5 The correlation between the cool X-ray gas and Hα emission detected by Kenney et al. 2008. (a) Temperature map of Figure 3.3d zoomed in to emphasize the temperature structure surrounding M86 and overlaid with contours of Hα imaging. There is a clear correlation of the Hα emission with the coolest gas phases in both the eastern and northern plumes extending out from M86. The centers of M86 and NGC 4438 are denoted by the cyan crosses. (b) The X-ray surface brightness image of Figure 3.1, zoomed in to the same region as (a). The Hα image contours are overlaid in cyan...... 80

xxvii 4.1 The distribution of X-ray no-source binomial probabilities, Pb, for all sources before inclusion in the final catalog. For this figure, probabilities were determined from counts in the full band (0.5 8.0 keV). The minimum threshold chosen for − 3 inclusion in the final catalog, Pb < 10− , is denoted by the dashed vertical line. For

presentation purposes, we set log Pb = 20 for all sources where log Pb 20. . . 90 − ≤ − 4.2 An example of spurious candidate sources initially identified within the central 1 ∼ ′ of MACS J1931.8-2634. Contours show the respective PSF region ( 90% EEF) ∼ for each candidate source. This particular cluster shows evidence for both a recent merger and powerful AGN feedback (Ehlert et al., 2011), which results in the sharp fronts and cavities in the 50 kpc surrounding the cluster center. The black source ∼ 3 has been identified as spurious automatically by ACIS-EXTRACT (Pb 10 ), ≥ − while the white sources had to be removed manually. Although these sources are significant compared to their local background, they are not AGN. Such candidate sources can only be reliably removed by visual inspection. The only true AGN source included in the final catalog is the magenta source at the center. This is one 43 1 of only a few luminous cD galaxies in this sample (LX 8 10 erg s ). . . . . 98 ∼ × − 4.3 The sensitivity map for MACS J1931.8-2634 in the 0.5-8.0 keV energy band, calculated by solving for the number of counts required to detect a source at a binomial 3 probability of Pb= 10− . These count estimates were converted to a physical flux assuming an absorbed power-law spectrum with a photon index of Γ= 1.4. For this particular cluster, the minimum full-band flux to which 50% of the area is sensitive is 1.8 10 15 erg cm 2 s 1 . The dark region at the center of the cluster shows that × − − − the local flux limit near the cluster is approximately 5-10 times higher than the surrounding region despite being the region with the sharpest PSF, due to the presence of the bright cluster “background” emission...... 103 4.4 The Chandra full-band counts image for the galaxy cluster MACS J1931.8-2634 (Ehlert et al., 2011), with the initial and final point source catalogs overlaid. The magenta ellipses correspond to the initial point sources detected by WAVDETECT,

while the blue circles correspond to point sources that satisfy the threshold Pb < 3 10− in the full band. The radius of the blue circle corresponds to 1.5 times the 99% EEF radius for the point source position. For this cluster, 162 initial candidate sources were detected by WAVDETECT. This was refined to a final sample of 137 3 high-confidence (Pb < 10− ) sources detected across all three bands...... 104

xxviii 4.5 Survey solid angle as a function of ﬂux limit in the soft, hard, and full bands for all cluster observations. In order to ensure survey completeness, we have only included the region within 15 arcminutes of the aimpoints in calculating the survey area presented here. The total survey area within 15 arcminutes of the aimpoints over these 135 clusters is 12.1 deg2...... 105

5.1 Cumulative number counts (log N log S ) in the full (0.5 8.0 keV, a), soft (0.5 − − − 2.0 keV, b), and hard (2.0 8.0 keV, c) energy bands for the cluster fields (black). − The red curves show the cumulative number counts in the same energy bands for the CDFS (Lehmer et al., 2012). The blue curves are the results from the COSMOS survey. The band-specific flux limits used to determine the radial distribution of X-ray point sources are denoted by the vertical dashed line in each figure. In all 14 2 1 three bands an excess of sources at fluxes & 10− erg cm− s− with respect to the control fields is observed...... 112

2 5.2 The projected density of X-ray bright point sources in all three bands, in units of deg− . In all three lines, the

solid black line corresponds to the best-ﬁt constant background density in the range 3-5 r500, and in all three cases

this background density is consistent with the expected ﬁeld source density derived from CDFS and COSMOS.

In all three energy bands, this constant background ﬁeld density is consistent with the expected ﬁeld density

determined from the CDFS and COSMOS data. (a): The surface density of X-ray bright full band sources (FX(0.5 − 8.0 keV) > 5 10 15 erg cm 2 s 1 ) as a function of radius, in units of r . A total of 6443 sources were included × − − − 500

in the calculation of this proﬁle. (b): The surface density of X-ray bright soft band (FX(0.5 2.0 keV) > 3 − × 15 2 1 10− erg cm− s− ) sources as a function of radius, in units of r500. A total of 3055 sources were included in

the calculation of this proﬁle. (c): The surface density of X-ray bright hard band sources (FX(2.0 8.0 keV) > − 14 2 1 10− erg cm− s− ) as a function of radius, in units of r500. A total of 2933 sources were included in the calculation

of this proﬁle...... 113

5.3 The projected density of excess X-ray point sources detected above a full band lumi- 43 1 2 nosity limit of L 3 10 erg s , in units of deg− . This projected source density ≥ × − follows a similar power-law model as that observed for the ﬂux-limited sample. . . 114

xxix 6.1 The projected number density proﬁle of X-ray point sources with FX(0.5 8.0 keV) > − 14 2 1 10− erg cm− s− with optical counterparts located within 2 arcsec of the X-ray

source position. Radii are scaled in units of r500. The magnitude range for the opti-

cal counterparts is RBCG 0.5 < R < 23, where RBCG corresponds to the magnitude − of the BCG for each individual cluster. Any X-ray point sources matched to BCG’s

are not included in this profile. The projected source density profile beyond r500 ∼ is consistent with COSMOS at the 95% confidence level. We caution, however, that the X-ray source density measured by COSMOS may be slightly overdense with respect to the cosmic mean. The maximum number of X-ray point sources in a radial bin is 26, and the minimum number is 6...... 123

6.2 The projected density of optically selected sources (RBCG 0.5 < R < 23) in galaxy − cluster ﬁelds, as a function of radius in units of r500. The dashed line denotes the expected density of ﬁeld galaxies from COSMOS, using the median BCG magnitude

of RBCG 0.5 = 18.83. A clear excess of galaxies corresponding to a 15% − ∼ overdensity above the COSMOS value is observed at distances of 2.5r500. BCG’s ∼ are not included in this proﬁle...... 125

6.3 The fraction of cluster+ﬁeld galaxies (RBCG 0.5 < R < 23, not including BCG’s) − 14 2 1 hosting X-ray bright (FX > 10− erg cm− s− ) AGN, as a function of radius in

units of r500. The dashed lines denote the field AGN fraction inferred from COS- MOS at the same limits for the X-ray flux and optical flux, using the median BCG

magnitude of RBCG,med 0.5 = 18.83. A trend that rises with clustercentric radius − is observed, which converges to expected ﬁeld value at distances of 2r500. . . . . 126 ∼

xxx Chapter 1

Introduction

The studies discussed in this thesis, which compare cluster galaxies to galaxies in the field requires a robust understanding of the thermodynamic properties of the ICM and the processes at work in field galaxies. In the introduction, the key astrophysics that drives our current understanding of galaxies, AGN, and galaxy clusters will be summarized and reviewed. While all of the theory discussed in this chapter may not apply directly to the data presented here, it is nevertheless important that these concepts be discussed and developed clearly. In §1.1, we will present an introduction to the large scale structure formation, in particular the formation of galaxy clusters; and in §1.2 we discuss the constituent components and thermodynamic structure of galaxy clusters. In §1.3 we introduce the key properties of AGN, and in §1.4 we discuss the unique astrophysical processes that influence the evolution of galaxies in clusters. In §1.5 we discuss the general trends by which galaxies and AGN in the field evolve, and in §1.6 we discuss the consensus view and general picture of galaxy evolution in clusters. Finally, we use §1.7 to discuss some of the key outstanding questions that this thesis will address and answer, at least in part.

Throughout this thesis, unless otherwise noted, X-ray energies will typically denote an energy range similar to the throughput of the Chandra or XMM-Newton X-ray telescopes, 0.1 10 keV. ∼ − For all of the work presented here, the assumed cosmological model is canonical ΛCDM where 1 1 Ωm = 0.3, ΩΛ = 0.7, w = w0 = 1, H0 = 100h km s Mpc− , and h = 0.7. For distances, − − the variable b will be used to denote projected distances, while the variable r will denote a 3- dimensional distances, usually from the center of a cluster. The variables DA and DL denote the angular diameter and luminosity distances to a source.

1 CHAPTER 1. INTRODUCTION

1.1 Introduction to Galaxy Clusters

In the modern theory of cosmological structure formation, matter coalesces in a “bottom-up” or hierarchical manner; where smaller scale structures as stars coalesce into larger and larger structures such as galaxies and galaxy clusters over cosmic time. These simulations all show that structures tend to form a large web of filamentary structures. The sites where different large scale structure filaments intersect with one another are where we find the most massive virialized structures in the Universe: galaxy clusters. They represent the sites of the largest initial overdensities in the cosmic density field, and are continuously accreting new mass as either galaxies or diffuse intergalactic gas. Because of this, it is not trivial to define the outer boundary of a galaxy cluster. A commonly utilized definition of an outer boundary is at a specific “overdensity” radius, r∆. The length scale r∆ is defined such that the enclosed mean mass density is ∆ times the critical density of the Universe at the redshift of the cluster. For galaxy clusters, the common overdensity radii used are r200, r500, and r2500. These three scale lengths correspond to physical lengths of 1 2 Mpc, 0.6 1.5 Mpc, ∼ − − and 400 600 kpc, respectively. Regions with an average density of 200 times higher than the ∼ − ∼ cosmic mean density at any particular redshift remain gravitationally bound despite the underlying expansion of the Universe.

1.2 Astrophysical Processes in Clusters of Galaxies

As the most massive gravitational potential wells in the Universe, the constituent components of galaxy clusters are subject to unique processes. In this section, we will discuss the general structure and key physics of the three main matter phases in galaxy clusters: dark matter, galaxies, and intracluster medium (ICM).

1.2.1 Dark Matter

The majority of the mass ( 85%) of the mass in galaxy clusters is in the dark matter component, ∼ although the precise nature of the dark matter remains uncertain. Cosmological simulations of structure formation have shown that dark matter halos on nearly all scales tend to follow a similar, empirically derived model for their density profile. This profile, first discussed in Navarro, Frenk & White (1995, 1997) and commonly known as the NFW profile,

2 1.2. ASTROPHYSICAL PROCESSES IN CLUSTERS OF GALAXIES

Figure 1.1: A simulated galaxy cluster, from the Millennium Simulation (Springel et al., 2005). This cosmological simulation utilized 21603 dark matter particles over a co-moving 1 volume of 500 Mpc h− . The majority of the structures have collapsed into large scale filaments. At the intersection of these filaments reside galaxy clusters, the initial sites of the largest perturbations of the cosmic density field, as shown as the center of this figure. Galaxy clusters are continuously accreting new galaxies and ambient gas from the surrounding medium which continues to virialize. Figure accredited to the Max-Planck-Institute for Astrophysics in Garching, Germany.

ρ (z)δ ρ(r) = c c (1.1) 2 (r/rs)(1 + r/rs) 2 where ρ(r) is the mass density, ρc(z) = 3H(z) /8πG is the critical density at redshift z, rs is the scale 3 radius, c is the concentration parameter (with c = r200/rs) and δc = 200c /3 [ln (1 + c) c/(1 + c)]. − For galaxy clusters, the dark matter halos are often well ﬁt to NFW proﬁles with measured concentration parameters of c 4(Navarro, Frenk & White, 1997; Bullock et al., 2001; Duffy et al., 2008; ∼ Gao et al., 2008).

1.2.2 Galaxies

Galaxies in clusters are only a small fraction ( 2%) of the total cluster mass, but have played ∼ a key role in our understanding of galaxy clusters, especially prior to the modern generation of X-ray observatories. In this section, the properties of the general population of cluster galaxies

3 CHAPTER 1. INTRODUCTION

will be discussed. We reserve discussion of the physical mechanisms by which these observational signatures arise to later in the introduction.

Red Sequence Identiﬁcation

Galaxy clusters are typically identified in optical imaging data using color-magnitude diagrams of the detected galaxies. As shown in Figure 1.2, galaxy clusters host large numbers of red galaxies that extend to bright magnitudes. This location in color-magnitude space is typically known as the red sequence, as it dominated by red galaxies with old stellar populations. While not all cluster member galaxies are located on the red sequence, a red sequence of galaxies is identified in galaxy clusters at nearly all redshifts with only a small intrinsic scatter. 1 Detection algorithms that search for overdensities of red galaxies are efficient at identifying candidate galaxy clusters in wide field optical imaging surveys such as SDSS (Koester et al., 2007) or the upcoming Dark Energy Survey (Rykoff et al., 2013).

Morphology-Density Relation

In addition to cluster member galaxies having considerably redder colors, on average, than field galaxies, it has long been observed that cluster member galaxies also have a different distribution of morphologies than field galaxies. This result, known as the morphology-density relationship, was first addressed in the work of astronomers in the early 20th century (Hubble & Humason, 1931; Morgan, 1961; Abell, 1965; Oemler, 1974) and made more quantitative by Dressler (1980). In these studies, it was observed that regions of high galaxy density tend to host higher fractions of elliptical galaxies. This relationship has held up in more recent studies that utilize larger and more complete samples of galaxies (e.g. Goto et al., 2003, 2004), the main result of which is shown in Figure 1.3.

Spatial Distribution of Cluster Galaxies

The projected density proﬁle of galaxies in clusters is commonly ﬁt to the empirically derived King model (King, 1962), which takes the form of

N N (b) = 0 (1.2) Gal 2 1 + b rc ³ ´ 1Given the typical SED of a cluster member galaxy, the particular ﬁlters where the red sequence is most readily observed does change strongly with redshift, and different color-magnitude diagrams are best-suited for clusters in different redshift ranges.

4 1.2. ASTROPHYSICAL PROCESSES IN CLUSTERS OF GALAXIES

which in 3 dimensions is equivalent to

N0 NGal(r) = (1.3) 2 3/2 1 + r rc µ ³ ´ ¶ although recent work has also utilized projected NFW proﬁles. In previous studies (e.g. Carlberg et al., 1997; Lin, Mohr & Stanford, 2004; Popesso et al., 2007; Budzynski et al., 2012), the typical measured core radius/scale radius for the galaxies in clusters is 0.2 0.3r500, corresponding to an ∼ − NFW concentration parameter of 2.5 4, and has been measured to be roughly a factor of two ∼ − lower (i.e. broader) than the underlying dark matter distributions, an effect most clearly measured in Budzynski et al. (2012).

Brightest Cluster Galaxies

At the center of nearly every galaxy cluster is typically one or two especially massive and bright galaxy known as the Brightest Cluster Galaxy or BCG. BCG’s are typically the most massive galaxies detected in the Universe, hosting stellar masses as large as 1012 M , roughly a factor of 100 ∼ ⊙ more massive than the Milky Way. Galaxies at these masses usually have early-type (i.e. elliptical) morphologies, and are dominated by old (red) stellar populations, although they can also be sites of recent star formation. BCG’s are commonly located at or near the bottom of the potential well for the entire cluster, and as such are usually embedded in the densest ICM and subject to the highest rates of galaxy-galaxy tidal encounters..

1.2.3 The Intracluster Medium

The ICM is a hot, diffuse plasma occupying the space between galaxies in clusters, out to distances of r200 or further. The majority of the baryonic mass in galaxy clusters is observed in the ICM, ∼ and the mass of the ICM accounts for roughly 15% of total mass in galaxy clusters. ∼ The depth and size of the cluster potential results in the ICM having a temperature of 107 8 K, ∼ − which corresponds to a characteristic photon energy of kBT 1 10 keV. At these temperatures, ∼ − the peak of the black-body spectrum is in the soft X-ray regime, which makes it ideally suited for observations by X-ray imaging telescopes including Chandra, XMM-Newton, and Suzaku. At these temperatures, nearly all elements up to iron are nearly fully ionized. The overall particle density of the ICM ranges from 10 1 to 10 5 cm 3, and the optical depth of the ICM is τ << 1. Subsequently ∼ − − − 5 CHAPTER 1. INTRODUCTION

the emission is optically thin, with practically no intrinsic absorption of the X-ray photons along the line of sight.

Thermal Bremsstrahlung Emission

Deep X-ray observations of the ICM have conﬁrmed that its emission spectrum is well-ﬁt to thermal bremsstrahlung emission with a temperature of T 107 8 K. Thermal bremsstrahlung emission ∼ − originates from the acceleration of electrons (with charge e and number density ne ) due to Coulomb interactions with neighboring ions (with charge Ze and number density ni). The energy emission at a particular frequency ν for thermal bremsstrahlung emission in an optically thin plasma at a 3 1 1 temperature T (in units of erg cm− s− Hz− ) is given by Rybicki & Lightman (1985) as

38 2 1/2 hν/kBT ǫν = 6.8 10− Z neniT − e− g¯ff (1.4) × whereg ¯ff is known as the Gaunt factor, and is a term of order unity that accounts for the quantum mechanical effects that arise when calculating the orbits of electrons around ions. It is important to emphasize that the temperature of the ICM measured in X-ray telescope data is the electron temperature (which dominate the emission), and not necessarily the temperature of the ions (which dominate the gas mass). Although these two temperatures should be identical in most sites in galaxy clusters, it is possible that gas located behind shock fronts or in the outskirts of clusters may not have had sufficient time to reach electron-ion temperature equilibrium. While not intrinsic to the clusters themselves, all galaxy cluster emission is subject to absorption by Galactic H I gas, which typically has a column density of 1019 22 cm 2. This intervening ∼ − − hydrogen efficiently suppresses emission of lower energy ( 0.1 1 keV) X-ray photons along the ∼ − line of sight.

Line Emission and the Origin of Metals in the ICM

In addition to this continuum component, line emission from metals such as O, Si, and Fe are also detected in the X-ray spectra of the ICM, with overall metal abundances that can range from 0.1 1.0Z . The most prominent emission lines are detected in the iron L and K complexes, ∼ − ⊙ which reside at 1 keV and 6.4/6.7 keV, respectively. Recent studies of the Perseus Cluster have ∼ demonstrated that its ICM has a nearly uniform metallicity (Z 0.3Z ) out to distances of r200 ∼ ⊙ ∼ (Werner et. al 2013 submitted, Urban et al. 2013 submitted), which suggests that the majority of the ICM is enriched with metals before it collapses into a virialized galaxy cluster. Within the central

6 1.2. ASTROPHYSICAL PROCESSES IN CLUSTERS OF GALAXIES

500 kpc of the cluster core, metal enrichment of the ICM by Type I and Type II supernovae are ∼ necessary to account for the measured abundance ratios of different elements (e.g. Dupke & White, 2000; de Plaa et al., 2007; Werner et al., 2008; Simionescu et al., 2009; Bohringer¨ & Werner, 2010; Million et al., 2011). Some examples of different theoretical spectra for thermal bremsstrahlung with line emission are shown in Figure 1.4.

Measuring the Thermodynamics of the ICM

The detectors aboard X-ray telescopes such as Chandra and XMM-Newton are able to measure the energies of incident X-ray photons with an energy resolution of 10 20% across their full energy ∼ − band. With such energy measurements, these instruments are able to extract emission spectra of the ICM that, when properly calibrated and background subtracted, determine the electron temperature and density of the ICM directly. There are sufficient photon statistics in a typical galaxy cluster observation to perform spatially resolved spectroscopy of the ICM. For the brightest clusters in the sky, there are sufficient X-ray counts to measure accurate temperatures in regions limited only by the angular resolution of the telescope. A few examples of theoretical X-ray emission spectra of the ICM are shown in Figure 1.4. By fitting the X-ray spectra of small regions with similar models, we are able to produce either 1-dimensional profiles or 2-dimensional “maps” of the thermodynamic structure of the ICM such as its temperature, pressure, and metallicity structure.

Temperature Structure of the ICM

Studies investigating the temperature, pressure, and entropy structures of galaxy clusters show that galaxy clusters can be readily divided into two distinct groups: cool-core clusters and non-cool core clusters. The differences in the central temperature and surface brightness’s between these two classes are striking. Astrophysically, this dichotomy arises appears to be driven primarily by the different cooling times in the central regions of these two cluster populations (Hudson et al., 2010), although open questions remain regarding the details of this dichotomy and the means by which clusters can transition between the two populations (Hudson et al., 2010). Since the emissivity of 2 1/2 the ICM scales as ǫν n T and the total thermal energy scales as neT, the rate at which the ∼ e ∼ ICM loses energy radiatively can be inferred to generally scale as n 1T 1/2. In the core region of a ∼ e− cluster, this time scale for cooling can be much quicker than the time since the last major disruptive

7 CHAPTER 1. INTRODUCTION

heating event. 2 More formally, the cooling time of the ICM can be calculated as

5(ne + ni)kBT tcool = (1.5) 2neniΛ(T) where Λ(T) is known as the cooling function and has been well measured in simulations (Sutherland & Dopita, 1993; Schure et al., 2009). The nominal rate at which central X-ray emitting ICM gas cools in absence of other heating sources is determined as

2 LXµmp M˙ = (1.6) 5 kBT 1 and in the most extreme cases these nominal cooling rates have been measured to reach 1000M yr− . ⊙ Cool-core regions of galaxy clusters have been observed to extend out to distances of R 50 ∼ − 100 kpc from the X-ray centroids (Bauer et al., 2005). Outside of this region, the temperature pro- ﬁles of galaxy clusters are usually consistent with a constant temperature over a large range of radii. The key differences in the surface brightness and temperature structure between cool core and non-cool core clusters is shown in Figures 1.5 and 1.6.

Density Structure of the ICM

If one assumes that the ICM is isothermal and that the gas and galaxies are in equilibrium in the same gravitational potential, then one can show that the gas density and the galaxy density proﬁles can be connected to one another with by

β 2 n (r) Ngal(r) µmpσ e = with β = r (1.7) ne(r = 0) Ã Ngal(r = 0)! kBT where σr is the radial velocity dispersion of the galaxies and µ = 0.62 is the mean molecular weight of a gas particle. Utilizing the King model for the galaxy density profile discussed above, the 3- dimensional gas density profile for this simplified model is given as

ne(r = 0) ne(r) = (1.8) 2 3β/2 1 + r rc µ ³ ´ ¶ which has typically been a good ﬁt to data outside of the core regions, at least to distances of r500. ∼ 2Most galaxy cluster cores are assumed to have had sufﬁcient time to cool if their central cooling time is shorter than the light travel time since z 1 2, corresponding to 7 10Gyr. ∼ − ∼ − 8 1.2. ASTROPHYSICAL PROCESSES IN CLUSTERS OF GALAXIES

The same model also applies to the projected surface brightness proﬁle, after changing the value of the β-index to 3β/2 3β/2 + 1/2. For more complex density distributions, it may be necessary → to include two or more beta models with independent values of their core radii, β-indexes, and normalizations in order to provide a suitable ﬁt to the data. It is also straightforward to transform this model into one that allows for two-dimensional structures with non-zero ellipticity.

= A ne(x, y) α , (1.9) + b(x,y)2 1 2 · rc ¸ where A is the central normalization, rc is the core radius, and α is the power index. The term b(x, y) is given as

2 2 2 (y y0) cos θ (x x0) sin θ b(x, y) = (x x0) cos θ + (y y0) sin θ + − − − , (1.10) − − " 1 ǫ # £ ¤ − and gives the distance of any point in the image (x, y) from a ﬁxed center (x0, y0) with an ellipticity ǫ and position angle θ. 2 The surface brightness of the galaxy cluster in X-rays scales with ne, and subsequently the X-ray emission from galaxy clusters rapidly decreases with radius.3 Given the known sources of background in X-ray telescopes such as Chandra and XMM-Newton, the cluster emission becomes indistinguishable from the background beyond distances of r500 for all but the most nearby and ∼ brightest clusters. Investigations into the thermodynamic structure of the ICM between r500 and r200 require the use of the Suzaku X-ray telescope, whose low-earth orbit has considerably lower and more stable background and can therefore detect fainter diffuse emission than either Chandra or XMM-Newton.

Pressure and Entropy Structure of the ICM

With measurements of the temperature and density of the ICM determined directly from spectroscopic modeling of the X-ray plasma emission, it is straightforward to determine additional thermodynamic properties of the gas. The thermal pressure of the ICM is deﬁned to be

P = nekBT (1.11)

3 2 For a typical β-model for the density with β 0.6, the density scales with radius as r− when r >> rc. The surface brightness therefore scales as r 4 ∼ ∼ ∼ − 9 CHAPTER 1. INTRODUCTION

while the entropy of the ICM gas is given as

2/3 K = kBTne− (1.12)

This second relationship is derived from the fact that the entropy of an ideal gas is typically given as γ S = kB ln Pρ , with γ = 5/3. By rearranging these terms, one can show that the entropy K deﬁned above has a one-to-one relationship with the formally deﬁned entropy of the ICM.

The pressure structure of the ICM has been inferred to follow the model of Nagai, Kravtsov & Vikhlinin (2007) P(x) P = 0 (1.13) γ α (β γ)/α P500 x (1 + x ) − where x = r/rs, and α, β, γ are the slopes of the pressure proﬁle at r rs, r >> rs, and r << rs, ∼ respectively. Fits to£ both X-ray¤ and SZ observations of galaxy clusters have demonstrated that this analytic model is a good description for a large number of galaxy clusters, although different parameters for the slope and normalization are required for cool core and non-cool core clusters. Cool core clusters also tend to have steeper entropy proﬁles in their central regions (e.g. Cavagnolo et al., 2009; Hudson et al., 2010; McDonald et al., 2013). In the absence of any heating sources beyond gravity, the entropy structure of the ICM has been predicted by simulations and scaling arguments to follow a power-law model (Voit, Kay & Bryan, 2005)

K(r) r1.1 (1.14) ∼

Such a model is typically a good ﬁt to the cluster regions between r2500 and r500, although ∼ ∼ there is clear evidence for heating and cooling in the central regions of clusters, especially in those clusters hosting cool cores (Cavagnolo et al., 2009; Hudson et al., 2010).

1.2.4 Determinations of Cluster Masses

We are able to calculate the total dark+luminous mass for galaxy clusters using measurements of the temperature and density structure observed in the X-rays. Assuming that the galaxy cluster is in hydrostatic equilibrium P = Φ (1.15) ∇ −∇ 10 1.2. ASTROPHYSICAL PROCESSES IN CLUSTERS OF GALAXIES

and that the cluster potential and temperature structure is spherically symmetric, we can use measurements of the cluster temperature and density to determine the shape of the underlying gravitational potential well and subsequently the enclosed mass. More concretely, the assumptions of an ideal gas, thermal pressure support, and spherical symmetry allow us to rewrite the equation for hydrostatic equilibrium as rkBT(r) d ln n d ln T M(r) = − + (1.16) Gµmp " d ln r d ln r #

Although such a calculation does not explicitly require any assumptions regarding the shape of the underlying gravitational potential or the temperature structure of the cluster (Nulsen, Powell & Vikhlinin, 2010), in practice the dark+luminous mass density profile is commonly parameterized as an NFW profile (see equation 1.1, Allen et al., 2008), which has been shown to be a satisfactory fit to the data in almost all instances (Schmidt & Allen, 2007). Additionally, the temperature and density structure of the ICM may be assumed to follow specific models such as those discussed above (e.g. Hasler et al., 2012; Landry et al., 2012), although these parameterizations may introduce important and unavoidable biases into the final determinations (Mantz & Allen, 2011).

Because masses calculated in this fashion explicitly assume hydrostatic equilibrium in the cluster potential well, they are commonly denoted as hydrostatic mass measurements. They are the most precise measurements of galaxy cluster masses available in the X-rays, but such a calculation can only be applied to clusters where the initial assumptions of hydrostatic equilibrium and spherical symmetry remain valid. These clusters are commonly denoted as relaxed clusters, as their morphologies are generally elliptical and show no signs of recent major disruptions such as mergers. More specifically, relaxed clusters can be readily identified observing their X-ray isophotes, which should show no centroid shifts between successive isophotes. Because relaxed clusters have been undisturbed on time scales comparable to the central cooling time, relaxed clusters often host cool cores and can also be identified by the peaked surface brightness profiles (Mantz et al. in prep). Mass determinations for large populations of galaxy clusters without follow up data usually take the form of scaling relationships that relate the cluster mass to observable properties such as the gas mass (Mgas), the temperature, or the luminosity. The scaling relations assume that galaxy clusters evolve in a self-similar manner, which is discussed and tested in Mantz et al. (2010a). This study showed that galaxy clusters therefore evolve self-similarly outside of their core regions to within 10%. ∼ While X-ray mass measurements are typically the most precise measurements of galaxy clusters at any wavelength, these masses can be biased, an effect that arises from primarily non-thermal

11 CHAPTER 1. INTRODUCTION

pressure not accounted for in the hydrostatic equations above. These effects are typically expected to be small (. 10 15% at all radii, e.g. Nagai, Kravtsov & Vikhlinin, 2007; Allen et al., 2008) but − remain uncertain. A joint X-ray plus weak gravitational lensing study of a representative sample of galaxy clusters will be critical to understanding the typical levels of bias in the X-ray cluster mass measurements. Optical mass measurements using weak lensing signals are signiﬁcantly less precise than X-ray masses, but should on average not be subject to any biases. This makes weak lensing studies ideally suited for cross-calibration cluster masses with X-ray observations, and several studies are underway to perform such tests (von der Linden et al., 2012; Kelly et al., 2012; Applegate et al., 2012).4

1.2.5 Testing Cosmological Models with Galaxy Clusters

The number density of galaxy clusters as a function of mass and redshift that one expects to observe depends very sensitively on the underlying cosmological model and initial spectrum of perturbations in the cosmic ﬁeld (for a review see Allen, Evrard & Mantz, 2011). By counting the number of structures observed in a given mass and redshift range, one can constrain the underlying cosmological model. Such tests have been performed using observations at multiple wavelengths, and the analysis framework is sufﬁciently developed that galaxy clusters can offer competitive constraints to other cosmological probes such as Type Ia supernovae and the CMB. Constraining cosmological parameters with growth of structure tests requires robust measurements of the total mass (luminous+dark matter) of these large scale structures, which are commonly derived through scaling relationships (see above). To describe the procedure of testing cosmological models with galaxy clusters in detail is beyond the scope of this thesis. Using a rigorous statistical framework, the most probable ensemble of cosmological parameters given the observed survey data is determined, taking into account values measured from other independent experiments (e.g. Vikhlinin et al., 2009; Mantz et al., 2010a,b).

4It should be noted that while proper weak lensing masses should be unbiased mass measurements on average, it is not straightforward to calibrate the optical data to the extent necessary to ensure an unbiased mass measurement. Even when using the same telescope data, considerable disagreements can and do arise between different weak lensing analyses.

12 1.3. OBSERVATIONS OF ACTIVE GALACTIC NUCLEI

1.3 Observations of Active Galactic Nuclei

Active galactic nuclei (hereafter AGN) are a unique class of galaxies where the central regions of the galaxy emit brightly across nearly the entire electromagnetic spectrum from MHz radio waves to TeV gamma rays, a range of more than 18 orders of magnitude in energy. By comparison, the majority of the emission from a normal galaxy is dominated by starlight, which spans roughly two order of magnitudes in energy between the near infrared to near UV (including the entire spectrum 6 10 of visible light). In general, AGN are powered by the supermassive black holes (M 10 − M ) • ∼ ⊙ that every galaxy likely hosts at its center. When these supermassive black holes accrete galaxy gas at sufﬁciently high rates, the resulting release of gravitational energy results in bright thermal and non-thermal emission from the galaxy center, the energetics of which can be comparable to that of the entire host galaxy. The maximum expected rate at which mass can accrete onto the black hole is typically deﬁned by the black hole’s Eddington limit. The Eddington limit, calculated as

4πGMmPc 4 M LEdd = = 3.2 10 L (1.17) σT × Ã M ! ⊙ ⊙ is the maximum luminosity at which a spherically symmetric mass can emit radiation without ex- ceeding its gravitational potential. The luminosity of accreting gas is connected to the rate at which mass is accreting onto the black hole (M˙ ) as

L = ηMc˙ 2 (1.18) where η is an efﬁciency term commonly assumed to have a value of η = 0.10. Although breaking spherical symmetry can allow for black holes to emit at luminosities above the Eddington limit, it is typically not greatly exceeded in even the most asymmetric conﬁgurations.

1.3.1 Taxonomy and Accretion Modes of AGN

Empirical classiﬁcation schemes of AGN have a long and storied history that predates a solid theoretical framework for the mechanisms that result in their emission, the full details of which are beyond the scope of this thesis. It is clear based on these classiﬁcation schemes, however, that there are few observational signatures ubiquitous to all AGN. Theoretical and observational studies have shown that the evolution of Active Galactic Nuclei

13 CHAPTER 1. INTRODUCTION

(AGN) and their host galaxies are linked to one another (e.g. Silk & Rees, 1998). The masses of the supermassive black holes at the centers of nearby galaxies correlate tightly with the masses of their galaxy bulges (e.g. Magorrian et al., 1998; Gebhardt et al., 2000; Marconi & Hunt, 2003), suggesting a connection between their growth histories. Feedback between AGN and their host galaxies has also been observed to play a key role in suppressing star formation (e.g. Kauffmann et al., 2003; Hopkins, 2004; Croton et al., 2006; Hopkins & Beacom, 2006; McNamara & Nulsen, 2007; Fabian, 2012). The co-evolution of supermassive black holes and their host galaxies is one of the most aggressively pursued questions in modern astrophysics, and a necessary component to all modern models of galaxy evolution. Attempts to unify the many observed source classes from a theoretical standpoint have made significant progress in modeling the observed AGN population, and suggest that the geometry of the accretion disk and central outburst with respect to the line of sight and the geometry of any intervening obscuring regions surrounding the disk play key roles in differentiating between many of the source classes. X-ray, optical, and radio studies have ultimately led to the conclusion that there are two fundamentally distinct AGN modes (e.g. Best et al., 2007; Hardcastle, Evans & Cros- ton, 2007; Merloni & Heinz, 2008; Best & Heckman, 2012; Fabian, 2012): a radiatively efficient mode associated with X-ray and optically-selected AGN (e.g. Alexander et al., 2003; Kauffmann et al., 2003; Bauer et al., 2004; Xue et al., 2010) and a radiatively inefficient mode, associated with strong jets and radio emission which is common in massive elliptical galaxies with hot X-ray halos (e.g. Allen et al., 2006; Balmaverde, Baldi & Capetti, 2008; Dunn et al., 2010; Best & Heckman, 2012; Pellegrini, Ciotti & Ostriker, 2012). While the radiatively efficient (or quasar) mode appears physically related to the accretion of cold gas onto the black hole, the radiatively inefficient (or radio) mode may be associated with the accretion of hot X-ray emitting gas. The kinetic energy injected into the surrounding environment by the jets emitted in this radiatively inefficient mode is, in principle, sufficient to prevent the cooling of the hot gas and suppress subsequent star formation (e.g. Allen et al., 2006; McNamara & Nulsen, 2007; Dunn et al., 2010; Fabian, 2012).

1.3.2 X-ray Emission of Active Galactic Nuclei

While AGN emit radiation at nearly all wavelengths, point-like X-ray emission is a common observational signature of AGN that may otherwise vary signiﬁcantly at other wavelengths. Although AGN are not always detected at X-ray wavelengths (e.g. Heckman et al., 2005), X-rays offer the most complete census and highest source densities of AGN at any wavelength (e.g. Brandt

14 1.4. GALAXY EVOLUTION IN CLUSTERS

& Hasinger, 2005). The typical X-ray emission spectrum of an AGN is well fit, to first order, by a power-law model, where the number of photons emitted per square-centimeter per second per unit energy, dN/dE, is given as dN Γ E− (1.19) dE ∼ with Γ known as the photon index of the emission. For most AGN, the value of Γ is typically 1 2, ∼ − and the canonical AGN spectrum has a photon index of Γ = 1.4 1.7(Xue et al., 2011, and refer- − ences therein). The origin of this continuum power-law X-ray emission is not well understood. It is widely accepted that the X-ray emission arises from the inverse Compton scattering of seed photons from the central accretion disk by a hot corona (e.g. Haardt & Maraschi, 1991; Reis & Miller, 2013), although the details of the model have not yet been fully developed. Fundamental properties of the corona including its geometry and location with respect to the accretion disk remain poorly constrained by observations (Reis & Miller, 2013). A generic corona model, however, predicts that the X-ray emission should have an intrinsic photon index of Γ 2, which only weakly depends on ∼ the corona’s temperature or optical depth (Haardt & Maraschi, 1991; Zdziarski, Poutanen & John- son, 2000; Kawaguchi, Shimura & Mineshige, 2001). The X-ray spectral properties of AGN have been observed to correlate with the bulk properties of the accretion flow. The photon index Γ, for example, has been observed to correlate with the luminosity of AGN normalized to their Eddington values (Shemmer et al., 2005, 2008). In addition to a non-thermal (power-law) continuum, AGN spectra are typically absorbed by one or more components of intervening gas. Galactic H I gas is always a source of absorption, and as discussed above the column densities of this absorption column are typically in the range of 1019 21 cm 2. Additionally, large intrinsic absorption well above ∼ − − the expected Galactic contribution are commonly observed in AGN spectra, and can reach column densities of 1024 25 cm 2. Emission lines are also commonly observed in the X-ray spectra of ∼ − − AGN, particularly the Iron K lines at 6.4 & 6.7 keV. These lines arise from the fluorescence of iron in the accretion disk, and typically originate at distances near the event horizon of the black hole.

1.4 Galaxy Evolution in Clusters

The local environment has been shown to play a key role in the evolution of galaxies both in clusters and in the ﬁeld. However, because of the higher densities of galaxies and the denser ICM, galaxies in clusters are subject to a number of unique astrophysical processes that do not typically occur in

15 CHAPTER 1. INTRODUCTION

ﬁeld galaxies. The processes discussed below, in conjunction with one another, are the origin of the morphology-density relationship and the red sequence. The precise contribution of each of these processes to the resulting galaxy cluster population, however, remains to be determined.

1.4.1 Ram Pressure Stripping

The key physical process that inﬂuences galaxies in clusters is known as ram pressure stripping, which arises from the relative motion of the galaxy gas with respect to the ICM. Simple estimates as to how the ICM may strip gas initially hosted by cluster member galaxies were ﬁrst discussed in Gunn & Gott (1972), and the key assumptions and calculations of this estimate will now be discussed in detail. Ultimately, gas is stripped from the galaxy if the pressure it experiences from its motion through the ICM is larger than the typical gravitational force binding it to the galaxy.

For any galaxy traversing through an external medium (such as the ICM) of density ρICM at a velocity v, the ram pressure it experiences is calculated as

2 Pram = ρICMv (1.20)

For a galaxy of radius R, the typical binding force per unit area (F) is commonly determined by the mean mass density of stars in the disk, Σ⋆, which typically dominates the total mass density in the disk. The binding force per unit area on the interstellar gas is therefore determined as

F = 2πGΣ⋆ΣISM (1.21) where ΣISM is the two-dimensional surface density of the interstellar medium (ISM). Stripping of the ISM therefore occurs wherever the density of the ICM satisﬁes the condition of

2πGΣ Σ ρ > ⋆ ISM (1.22) ICM v2 5 1 Using the values similar to those of the Milky Way and a typical velocity of v = 1, 000 km s− , 27 3 the resulting critical density for ram pressure stripping is ρICM > 4.6 10 g cm , or a particle × − − density of 10 3 cm 3, comparable to the density observed in galaxy clusters out to distances of ∼ − − r500 or further. The time scale for ram pressure stripping is of an order of 10 100 Myr (?), ∼ ∼ − which is signiﬁcantly faster than the galaxy crossing time in a typical cluster ( 1Gyr). ∼ 5The Milky Way has a stellar mass of 5 1010 M and an ISM mass of 5 109 M spread over a disk with a radius of 10 kpc (Mo, van den Bosch & White× , 2010⊙ ). × ⊙

16 1.4. GALAXY EVOLUTION IN CLUSTERS

While simulations have shown that the simple analytic estimate presented above is fairly accurate in predicting the radius of the gas disc that undergoes ram pressure stripping, more complex physics is required to account for the morphology of the stripped gas tails and their multiwavelength properties (e.g. Roediger & Bruggen¨ , 2006, 2007, 2008b; Tonnesen & Bryan, 2010; Ton- nesen, Bryan & Chen, 2011; Tonnesen & Bryan, 2012). The viscosity of the ICM (Roediger & Bruggen¨ , 2008a), turbulence, and magnetic fields (Ruszkowski et al., 2012) are all expected to influence the morphology of the stripped gas and its evolution after it is deposited into the ICM. The relative contributions of these processes remain uncertain. Observationally, ram pressure stripping is commonly observed in cluster member galaxies at multiple wavelengths, although the particular observational signatures of ram pressure stripping vary from site to site. For example, long tails of X-ray emitting gas at temperatures of 0.5 1 keV ∼ − are observed to trail behind giant elliptical galaxies such as M84 (Randall et al., 2008; Ehlert et al., 2013), NGC 4552 (Machacek et al., 2006a), NGC 4472 (Kraft et al., 2011), and M86 (Forman et al., 1979; Fabian, Schwarz & Forman, 1980; Nulsen, 1982; White et al., 1991; Finoguenov et al., 2004; Randall et al., 2008; Ehlert et al., 2013), and can extend for distances approaching 100 kpc or ∼ further. This stripped gas likely originates from the X-ray emitting gas associated with the halos of massive elliptical galaxies. Ram pressure stripping also transforms the gas reservoirs of spiral galaxies in clusters. Spiral galaxies in the Virgo Cluster such as NGC 4388 and NGC 4438 have been observed to be the sites of ram pressure stripping, although in these less massive galaxies the stripped gas is predominantly detected through tails associated with colder ( 10 104 K) gas, such ∼ − as Hα or CO line emission (Oosterloo & van Gorkom, 2005; Kenney et al., 2008; Dasyra et al., 2012). The ultimate fate of the stripped gas after it has been separated from its galaxy remains uncertain, as the microphysical properties of the stripped gas and the ICM dominate its evolution. Magnetic fields in the stripped gas, for example, may suppress thermal conduction between the stripped gas and the surrounding ICM, while turbulence may accelerate the mixing process. We will discuss the signatures and implications of ram pressure stripping at one site in more detail in Chapter 3. An example of ram pressure stripping observed at optical and infrared wavelengths in the Virgo Cluster galaxy NGC 4402 is shown in Figure 1.7. The term “ram pressure stripping”, as discussed in Gunn & Gott (1972), strips the galaxy of both its cold, strongly bound central gas and its hotter, more loosely bound halo gas. However, simulations and observations of galaxies in clusters using more recent and sophisticated models for the galaxies and ICM suggest that gas is not so effectively stripped by ram pressure (e.g. Larson, Tinsley & Caldwell, 1980; Bekki, Couch & Shioya, 2002). More recent models have predicted

17 CHAPTER 1. INTRODUCTION

that while the majority of galaxy gas may remain bound, the accretion of new gas onto the galaxy may shut off. It has also been suggested that only hot, diffuse halo gas may be stripped from galaxies by ram pressure while the colder central gas remains bound. In both of these cases, star formation is still suppressed in cluster member galaxies on longer time scales. Star formation in cluster galaxies therefore is suppressed on the gas depletion time scale (Mgas/SFR, 1 Gyr, as both ∼ of these processes starve galaxies of gas reservoirs that can replenish the cold gas processed by star formation. This process of slowly cutting off star formation is commonly denoted as strangulation in order to distinguish it from the more severe form of ram pressure stripping ﬁrst proposed in Gunn & Gott (1972), even though the same hydrodynamic process of ram pressure stripping may ultimately responsible in both instances. We will use the term “ram pressure stripping” for both of these variants, and only identify the nature of the stripping as strangulation as needed. In these cases, we may also refer to models that strip hot and cold gas on short time scales as classical ram pressure stripping models.

1.4.2 Harassment

The significant overdensities of galaxies in clusters drastically increase the rate at which galaxies interact with one another tidally. However, given the large relative velocities of galaxies in clusters compared to their internal velocity dispersion6, galaxy mergers are relatively rare in clusters. The expected merger rate between cluster galaxies is expected to scale with the cluster velocity dispersion σ as σ 3. High velocity encounters, however, typically heat the galaxy and raise its internal ∼ − energy (Moore et al., 1996; Moore, Lake & Katz, 1998; Moore et al., 1999; Mo, van den Bosch & White, 2010), which subsequently makes the constituent stars and gas less strongly bound. When multiple galaxy-galaxy encounters occur on sufficiently short time scales, the cummulative effect can transform a galaxy’s morphology and hot gas halo. This process is commonly known as harassment, and harassment and other tidal interactions have been predicted to trigger starbursts near the virial radii of galaxy clusters (e.g. Moore, Lake & Katz, 1998). Harassment is commonly inferred to be a key component of the morphology-density relationship, as ram pressure stripping effects do not directly influence the morphologies of cluster member galaxies. The transformation of galaxies from spirals to ellipticals are most efficiently performed via galaxy mergers and tidal interactions and heating of the stellar disk. Simulations of harassment

6 1 The internal velocity dispersion of stars and gas in galaxies is typically 100 km s− , while the velocity dispersion of galaxies in clusters is 1000 km s 1. ∼ ∼ − 18 1.4. GALAXY EVOLUTION IN CLUSTERS

have predicted the transport of galaxy gas towards the centers of galaxies, and in some instances this process has been inferred to trigger central starbursts (Moore, Lake & Katz, 1998; Moore et al., 1999).

1.4.3 Central AGN Feedback

As discussed above, the radiative cooling time for the ICM near cluster centers can become much shorter than the time since the last major merger. In the absence of external sources of heating, the high emission of dense gas should lead to very rapid cooling and very high rates of mass deposition 1 onto the BCG (up to 1000M yr− ), in turn causing very high star formation rates and strong ∼ ⊙ line emission around 0.5-1.0 keV (e.g. Fabian & Nulsen, 1977; Cowie & Binney, 1977; Peterson & Fabian, 2006; McNamara & Nulsen, 2007). However, observations with the most recent generation of X-ray, ultraviolet (UV), and optical telescopes including Chandra and XMM-Newton have not detected the obvious observational signatures associated with these “catastrophic cooling flows” (e.g. Peterson et al., 2001, 2003), and provides compelling evidence that some central source of heating must be present in these clusters. The most plausible source of heating is feedback from the brightest central galaxy (BCG) and its AGN, and several observational signatures of such feedback have been detected in cool core clusters. Large X-ray cavities filled with radio plasma emitted by the jets of accelerated electrons are clearly seen in many systems, which provide an expected source of heating and turbulence (e.g. Bruggen¨ & Kaiser, 2002). In systems such as the Perseus, Virgo, Centaurus, and Hydra A Clusters (e.g. Fabian et al., 2003; Forman et al., 2005; Nulsen et al., 2005; Fabian et al., 2006; Forman et al., 2007; Sanders & Fabian, 2007, 2008; Simionescu et al., 2009; Million et al., 2010a; Werner et al., 2010), heating of the ICM has been argued to involve sound waves and weak shocks, as shown in Figure 1.8. BCG’s commonly host large masses of hot halo gas. Resultingly, as discussed above, the AGN in these central systems are typically radio mode AGN, where the radiative power output is 100 1000 times smaller than that powering the jets. An image of the most energetic radio-loud ∼ − AGN currently know in shown in Figure 1.9, where the jet are inflating cavities in the ICM with a power of 1046 erg s 1 . Measured spectroscopic X-ray cooling rates are commonly of order 1% ∼ − ∼ of their inferred nominal cooling rates (e.g. McDonald et al., 2010). Residual star formation of these cooling gas phases is present, and it has been measured that roughly 15% of the gas observed to ∼ be cooling from X-ray temperatures spectroscopically ultimately forms stars (e.g. McDonald et al., 2011).

19 CHAPTER 1. INTRODUCTION

We emphasize that heating from central AGN feedback appears to be a ubiquitous process in cool-core galaxies, and such heating is essential to account for the multiwavelength structure of cool core galaxy clusters. With the exception of the higher redshift Phoenix Cluster (McDonald et al., 2012), practically every cool core cluster observed has spectroscopic cooling rates and central star formation rates considerably lower than what their nominal cooling rates (Equation 1.6) predict.

1.5 Evolution in AGN in the Field

The modern era of telescopes has allowed for new wider and deeper surveys of galaxies and AGN to be undertaken at a number of wavelengths (see e.g. Brandt & Hasinger, 2005; Brandt & Alexander, 2010, for reviews). These surveys have painted a complex picture as to the co-evolution of galaxies and their supermassive black holes, in particular for the most active sources. The three representative field surveys that will be discussed in the most detail in this thesis are the Chandra Deep Field North and South (CDFN and CDFS, respectively Brandt et al., 2001; Giacconi et al., 2002; Alexan- der et al., 2003; Luo et al., 2008; Xue et al., 2011) and the Cosmic Evolution Survey observed with the Hubble Space Telescope, the Subaru Telescope, XMM-Newton, and Chandra among oth- ers (COSMOS, e.g. Brusa et al., 2007; Cappelluti et al., 2007; Hasinger et al., 2007; Scoville et al., 2007b,a; Taniguchi et al., 2007; Elvis et al., 2009; Puccetti et al., 2009). Although both of these fields have observations with a large number of telescopes ranging from radio to Chandra X-ray wavelengths, this thesis will primarily focus on the Chandra observations of the CDFS and COS- MOS and Subaru observations of COSMOS. The CDFN and CDFS are narrow field ( 0.1 deg2) ∼ surveys with extremely deep Chandra exposure times (2 and 4 Ms, respectively). The COSMOS survey, on the other hand, has a smaller depth with Chandra( 100 200 ks of effective exposure ∼ − time), but a much larger survey area ( 0.87 deg2). ∼ These deep field surveys demonstrate that the majority of the X-ray point sources detected with Chandra are in fact AGN (Lehmer et al., 2012). The source density of AGN can approach 2 17 2 1 10, 000 deg− at the faintest fluxes ( 10 erg cm s ). These densities far exceed the number ∼ ∼ − − − density of AGN detected a other wavelengths, making X-rays the ideal wavelength to conduct large and complete surveys of AGN. Smaller contributions to the X-ray point source population are hosted in starburst galaxies, normal galaxies, and Galactic stars (see Figure 1.10, from Lehmer et al., 2012).7

7Normal galaxies that do not host AGN become a signiﬁcant population of X-ray point sources only at the very faintest ﬂuxes observable in the 4 Ms CDFS(Lehmer et al., 2012).

20 1.5. EVOLUTION IN AGN IN THE FIELD

3 The X-ray luminosity function (XLF, Φ(L), in units of Mpc− ) of AGN deﬁnes the differential comoving density of AGN at a given luminosity and redshift, typically denoted as

∞ dΦ(L′) Φ(L) = d log L′ (1.23) Zlog L d log L′

dΦ(L) The differential luminosity function dlog L is parameterized as one of several functions. For AGN, a double power-law model is commonly utilized

dΦ(L , z) A X = 0 (1.24) γ1 + γ2 d log LX [LX/L∗(z)] [LX/L∗(z)] with additional terms to account for evolution of the AGN population. Early studies assumed evolutionary terms that have commonly taken two forms: either Pure Density Evolution (PDE) or Pure Luminosity Evolution (PLE). These two models assume different scaling of the XLF with redshift. Speciﬁcally, PLE assumes that the XLF evolves as

dΦ(L , z) dΦ(L /e(z), 0) X = X (1.25) d log LX d log LX while PDE assumes a model that follows

dΦ(L , z) dΦ(L , 0) X = X e(z) (1.26) d log LX d log LX in both instances the redshift correction factor e(z) is given as

p (1 + z) 1 : z < zc e(z) =  1+z p2  e(zc) + : z zc  1 zc ³ ´ ≥  More recent surveys (Ueda et al., 2003; Silverman et al., 2008) suggest that neither of these models is an appropriate fit to the XLF, and have developed a Luminosity-Dependent Density Evolution (LDDE) model. In particular, PLE and PDE models tend to significantly over-predict the cumulative number counts of X-ray AGN expected in deep field surveys. This model takes the same form for the redshift correction as the PDE model described above, but allows the transition radius zc to change with luminosity. In this model, the transition redshift changes with luminosity as

zc∗ : LX La zc(Lx) = α ≥  LX  zc∗ : LX < La  La ³ ´  21 CHAPTER 1. INTRODUCTION

for some transition luminosity La. The best fit model parameter values from Ueda et al. (2003) provide values for all of the parameters in the fit. The key parameter values for AGN at low redshifts are = = +0.34 the redshift correction terms of zc∗ 1.9 and p1 4.23 0.27. These two terms together indicate that − at the redshift of the galaxy clusters in this study, the co-moving density of AGN should passively evolve as (1 + z)4. An example of the XLF fit to real data from Ueda et al. (2003) is shown in ∼ Figure 1.11.

Using these models for the XLF, it has been determined that the comoving spatial density of the most luminous X-ray AGN peaks at redshifts of z 2, similar to what is observed for galaxy ∼ star formation (Behroozi, Wechsler & Conroy, 2013, and references therein). The redshift of peak density decreases with decreasing luminosity, as shown in Figure 1.12. For this reason, AGN are often described as evolving in an “anti-hierarchical” or “top-down” manner, in contrast to the hierarchical formation of large scale structures in the Universe.

X-ray selected AGN are predominantly hosted in the most massive galaxies, with stellar masses 10 (M⋆) of M⋆ & 10 M . Plotting the host galaxies of X-ray AGN on the color magnitude diagram ⊙ demonstrates that they occupy the same region as normal galaxies with similar stellar masses, and also have morphologies similar to comparable galaxies that do not host AGN (e.g. Reichard et al., 2009; Tal et al., 2009). The clustering statistics of X-ray AGN in the field (see Cappelluti, Allevato & Finoguenov, 2012, for a review) indicate that the majority of X-ray AGN are hosted in dark matter 13 1 halos with masses of M 10 M h− , a value that is consistently measured out to redshifts of z 2. ∼ ⊙ ∼ Although it is clear from these studies that the environment that hosts AGN is reasonably well constrained to be similar out to redshifts of z 2, the particular physical mechanism that triggers ∼ X-ray AGN is not well understood. Studies of the two-point correlation functions of X-ray AGN in the field suggest that different mechanisms may dominate at different redshifts (see Cappelluti, Allevato & Finoguenov, 2012, for a review). Major galaxy mergers are likely the most important 44 1 mechanisms for fueling quasars at the highest luminosities and redshifts (L & 10 erg s− , z & 1, e.g. Hopkins & Beacom, 2006; Hopkins et al., 2008; Hasinger, 2008). At lower redshifts (z . 1), bar instabilities and less extreme galaxy-galaxy interactions are inferred to be more efficient at producing AGN (e.g. Georgakakis et al., 2009). However, further studies are required to understand why only the most massive galaxies are overwhelmingly the sites of X-ray AGN, as well the precise mechanism that triggers X-ray AGN in galaxies.

22 1.6. STAR FORMATION AND AGN IN CLUSTER GALAXIES

1.5.1 Connecting X-ray and Optical AGN

Similar trends are observed for optically selected AGN in both the overall shape of the luminosity function (Croom et al., 2004; Ross et al., 2012; Palanque-Delabrouille et al., 2013) and their host galaxy properties (e.g. Kauffmann et al., 2003), suggesting that both optical and X-ray AGN are fueled by cold central gas. Other studies have provided further evidence for a connection between optical and X-ray AGN. Both X-ray and optically selected AGN are typically observed in massive 10 galaxies (M⋆ > 10 M , e.g. Kauffmann et al., 2003; Xue et al., 2010). The X-ray and optical ⊙ luminosities of AGN, at least at low redshifts (z < 1), correlate with one another (Heckman et al., 2005) and their host galaxies tend to have massive, young stellar bulges (Kauffmann et al., 2003). The connection between optical and X-ray AGN does not hold universally, however, as many AGN with bright optical line emission are not observed to host X-ray sources (e.g. Heckman et al., 2005). It is also clear from X-ray AGN surveys that the opposite is true, i.e. that many X-ray selected AGN are observed as normal galaxies at optical wavelengths (e.g. Xue et al., 2010). The consensus view for such a discrepancy between the X-ray and optically selected AGN samples is primarily driven by the optical depth and geometry of any intrinsic absorbing material. Optical line emission from AGN is known to depend strongly on the geometry of the emitter, in particular the angle that the accretion disk makes with respect to the line of sight. Additionally, the presence of dust and other obscuring materials (which have an unknown geometry themselves) also can obscure the optical and/or X-ray emission.

1.6 Star Formation and AGN in Cluster Galaxies

With a theoretical and observational foundation as to the evolution of galaxies and AGN in the ﬁeld now in hand, we now discuss the trends we observe for galaxies and AGN in clusters and how they differ from ﬁeld galaxies at similar wavelengths. We discuss the most recent results addressing these questions and their implications regarding the astrophysical processes of galaxies in clusters below.

Star Formation in Cluster Galaxies

The morphology-density relation and higher fractions of red sequence galaxies demonstrate the importance of the cluster environment on the evolution of cluster member galaxies, and both of these signatures suggest that the this environment is efﬁcient at suppressing star formation in cluster galaxies. It has been observed that the fraction of star-forming galaxies in clusters and the average

23 CHAPTER 1. INTRODUCTION

star formation rates of galaxies in clusters are shown to both decline in towards the centers of galaxies clusters (von der Linden et al., 2010; Wetzel, Tinker & Conroy, 2012), mainly due to the inﬂuence of ram pressure stripping.

Measurements of the time scales over which the star formation is suppressed in cluster galaxies has indicated that star formation is shut off on time scales of 1 5 Gyr (e.g. Weinmann et al., ∼ − 2006; van den Bosch et al., 2008a,b; Weinmann et al., 2009). This time scale strongly favors a model where colder central gas remains bound to galaxies during infall(i.e. strangulation). In fact, it appears as though ram pressure stripping does not remove large masses of gas from galaxies until the central 0.5r500 of clusters. Early-type galaxies hosted in clusters, for example, often ∼ possess bright X-ray gas halos (e.g. Fabbiano, 1989) regardless of their clustercentric distance. As discussed above, a large diversity of ram pressure stripping effects can be directly observed for galaxies in nearby clusters. Both hot and cold gas tails are observed trailing behind cluster galaxies (Machacek, Jones & Forman, 2004; Oosterloo & van Gorkom, 2005; Kenney et al., 2008; Randall et al., 2008; Ehlert et al., 2013). In a few instances, the cold gas stripped from galaxies (such as that trailing behind the galaxy ESO 137-001, Sun, Donahue & Voit, 2007) can become a site of in situ star formation taking place outside of the galaxy within the ICM. For cluster member galaxies near the centers of clusters, ram pressure stripping appears to be ubiquitous and ongoing, although less severe than estimates for “classical” ram pressure stripping. In addition to slow quenching of star formation in cluster galaxies, there is also evidence from deep optical imaging of new central starbursts being triggered in galaxies in galaxy clusters near their virial radii (e.g. Moran et al., 2005). These starbursts are predicted to be triggered by harassment (Moore, Lake & Katz, 1998), although the rate at which such starbursts are triggered are not well constrained.

Optical AGN in Clusters

The fraction of galaxies hosting optically bright AGN is typically inferred to be lower in the centers of clusters as compared to the cluster outskirts (von der Linden et al., 2010; Pimbblet et al., 2013), and are inferred to be due to the same physical processes that suppress star formation in cluster galaxies. However, there is evidence that the fraction of star forming galaxies hosting bright optical AGN may be the same in the cluster environment as in the ﬁeld (von der Linden et al., 2010).

24 1.6. STAR FORMATION AND AGN IN CLUSTER GALAXIES

X-Ray AGN in Clusters

Comparisons between the cluster and field populations of AGN using X-ray observations have led to differing conclusions, and have primarily investigated whether or not X-ray point sources are more frequently observed in the vicinity of galaxy clusters. Cappelluti et al. (2005) identify an excess of point sources in four separate cluster fields relative to expectations from field surveys, although for six other cluster fields in their study no such excess was seen. Excesses have also been observed in particular cluster fields such as 3C295, RX J003033.2+261819 (Cappi et al., 2001), and MS 1054-0321 (Johnson, Best & Almaini, 2003), but no excess of X-ray point sources was detected in the vicinity of galaxy clusters observed in the large field ChaMP survey (Kim et al., 2004). Other studies using larger samples of galaxy clusters have argued for a statistical excess in their fields, and have shown that the excess of sources is primarily located within the central 1-2 Mpc of each cluster (e.g. Ruderman & Ebeling, 2005; Gilmour, Best & Almaini, 2009). Multiwavelength studies of cluster galaxies incorporating optical spectroscopy along with X-ray analysis have suggested that the fraction of galaxies hosting X-ray bright AGN in clusters may increase substantially with redshift, by a factor of 8 (e.g. Martini, Mulchaey & Kelson, 2007; Martini, Sivakoff & Mulchaey, ∼ 2009; Lehmer et al., 2013; Martini et al., 2013), an effect that was also observed in the large Bootes¨ field (Galametz et al., 2009).

The key limitation of such a study utilizing the X-ray data alone is the inability to identify which AGN are cluster members and which are field members. Determining the discrepancies between the cluster and field population of AGN therefore must be performed on a statistical basis until complete spectroscopic surveys of these same cluster fields are undertaken. Additionally, as discussed in the papers listed above as well as the following chapters, the number of X-ray point sources in a typical Chandra or XMM-Newton field intrinsic to the cluster is not large compared to the expected number of point sources expected from field sources along the same line of sight.8 One final limitation of utilizing X-ray data alone for a study of the AGN population is that normal galaxies are not readily detected, so comparisons between the overdensities of X-ray AGN and underlying galaxies cannot be performed without additional multiwavelength data. A joint X-ray + optical survey of galaxy clusters is therefore necessary to properly place any results regarding the X-ray AGN population in clusters into the proper context given the underlying galaxy population.

8As will be discussed in detail in later sections, in a typical Chandra exposure, roughly 50-200 ﬁeld sources and only 1-5 sources associated with the cluster are expected.

25 CHAPTER 1. INTRODUCTION

1.7 Key Tests in this Study

Although we have made great progress in understanding the mechanisms by which galaxies in clusters are subject to unique environmental effects, key questions remain regarding the precise nature and impact of ram pressure stripping, harassment, and central AGN feedback on galaxy evolution. For example, it is important to determine the extent that AGN feedback may be able to counteract the most extreme nominal cooling flows observed: does the AGN inject sufficient energy 1 to overcome the X-ray cooling flows of 1000M yr− ? And does the nature of AGN feedback of ∼ ⊙ that magnitude differ from feedback for weaker cooling flows? And while it is clear that the ICM transforms the gas reservoirs of cluster member galaxies, the joint optical and X-ray observations of galaxies in are not consistent with simple models of ram pressure stripping, especially with regards to the fate of the stripped gas as it mixes with the surrounding ICM. It is also clear from the discussion above that X-ray observations of galaxies, AGN, and galaxy clusters play a critical role in improving our understanding of these processes. X-ray observations not only provide the clearest picture of the morphology and thermodynamic structure of the ICM, but also are an excellent wavelength for observing the hot halo gas and AGN hosted by cluster member galaxies. The X-ray data are a key aspect to any multiwavelength study that hopes to observe the full range of behavior of galaxies in clusters. Each of the following chapters will address an aspect of galaxy evolution in clusters using primarily X-ray observations of galaxy clusters spanning from the nearby Virgo Cluster (at a distance of 17 Mpc) to galaxy clusters out to redshifts of approaching unity (a comoving distance of over 3000 Mpc). These data will be supplemented by optical and radio observations of these same galaxy clusters. With these data, we will specifically address the myriad ways in which we use X- ray observations in conjunction with other multiwavelength telescope data to better understand the physical processes by which galaxies and the ICM co-evolve with one another in galaxy clusters.

26 1.7. KEY TESTS IN THIS STUDY

Figure 1.2: The red sequence of cluster member galaxies identified using color magnitude 1 diagrams for all sources located within 2h− Mpc surrounding two separate galaxy clusters, from Koester et al. (2007). These diagrams are derived using SDSS photometry in the g,r, and i bands. The largest plus signs indicate the BCG’s while the smaller plus signs denote galaxies identified as cluster members Top: Color-magnitude diagram for Abell 2142, which is at a redshift of z = 0.0942. Bottom: Color-magnitude diagram for the more distant cluster Abell 1682 (z = 0.23). In this higher redshift cluster, the red sequence has moved rightward (fainter fluxes) and upward (redder colors) due to cosmic expansion.

27 CHAPTER 1. INTRODUCTION

Figure 1.3: The morphology-density relationship, as determined by Goto et al. (2003), which shows the different galaxy populations as function of environment density. The red dashed line denotes the fraction of galaxies that are classiﬁed as early-type, i.e. elliptical galaxies. The blue dashed line denotes the fraction of galaxies that are late-type, i.e. spiral galaxies. The green solid line and teal dotted line denote the fractions of galaxies identiﬁed as intermediate- type and early-type disk galaxies, respectively. It is clear from these data that spiral galaxies are preferentially found in sparse environments while elliptical galaxies are observed at higher rates in dense environments like galaxy clusters.

28 1.7. KEY TESTS IN THIS STUDY

−5 kT=0.5 keV

10 kT=1.5 keV kT=3.0 keV −1 keV −1 −6 s 10 −2 Photons cm −7 10

0.1 1 10 Energy (keV) (a)

−5 20 −2 nH=2 x 10 cm 10

−5 20 −2 Z=0.1 Solar nH=5 x 10 cm 21 −2 10 Z=0.3 Solar nH=1 x 10 cm Z=1.0 Solar −1 −1 −6 keV keV 10 −6 −1 −1 s s 10 −2 −2 −7 10 Photons cm Photons cm −7 10

0.1 1 10 0.1 1 10 Energy (keV) Energy (keV) (b) (c)

Figure 1.4: Theoretical spectral models for the Intracluster Medium calculated using the MEKAL model (Kaastra & Mewe, 1993) in XSPEC (Arnaud, 2004), which calculates the emission from a hot, single temperature, optically thin plasma with metals. We use the photoelectric absorption model of Balucinska-Church & McCammon (1992), and the solar metallicity abundance table of Anders & Grevesse (1989). All of these spectra are shown in the rest frame (z = 0) and have the same emission measure. Each sub-ﬁgure shows how the emission spectrum changes based on variations of a single parameter. a) Emission spectra models where the temperature is varied from 0.5 keV (red curve) to 3.0 keV (blue curve). It is clear from these models that higher temperature emission not only results in broader continuum observable to higher energies, but the relative emission of different iron lines varies dramatically with temperature. The bump near 1 keV in the kBT = 0.5 keV model is due to Fe-L emission lines. b) Emission spectra models where the overall metallicity is varied∼ from 0.1Z (red curve) to 1.0Z (blue curve). Varying the metallicity at this temperature results in varying prominence of the Fe-L complex⊙ lines (at energies near⊙ 1 keV) and the Fe-K line (at an energy of ∼ 20 2 6.4 keV). c) Emission spectra where the intervening Galactic absorption column density is varied from 2 10 cm− (red curve) 21 2 × to 1 10 cm− (blue curve). The exponential cutoff of observed photons below 1 keV is clearly shown, and the precise cutoff energy× increases with absorption column depth. 29 CHAPTER 1. INTRODUCTION

(a) (b)

Figure 1.5: Comparisons of the 2-dimensional surface brightness distributions of two galaxy clusters: a cool-core cluster (Abell 2029, left) and non cool-core (Abell 2319, right), from Million & Allen (2009). Both clusters have been re-scaled to account for their different redshifts. Cool core clusters (such as Abell 2029) have very pronounced surface peaks, while non cool-core clusters do not. The centers of cool core clusters typically have high densities of ICM ( 0.1 cm 3), which enable it to cool rapidly (. 1 Gyr). Since the surface brightness ∼ − scales as n2 and undergoes a cooling ﬂow that increases its density further, these regions are ∼ e dramatically brighter than the surrounding regions which do not have sufﬁcient time to cool.

30 1.7. KEY TESTS IN THIS STUDY

Cool Core

Scaled Temperature Non−Cool Core 0.4 0.6 0.8 1 0.01 0.1

Radius (r/r500) Figure 1.6: The differences between scaled temperature profiles of cool-core and non cool- core clusters from Sanderson, Ponman & O’Sullivan (2006). This profiles shows the average temperature profile of galaxy clusters after dividing by their mean temperatures (measured in the radial range of 0.1 0.2r500). It is clear that while non-cool core clusters are broadly − consistent with a single temperature across their radial range, cool core clusters show a marked drop in their central regions.

31 CHAPTER 1. INTRODUCTION

Figure 1.7: The small scale inﬂuences of ram pressure stripping in the Virgo Cluster spiral galaxy NGC 4402, from Crowl et al. (2005). The bowed shape of the galaxy disk, combined with the north-south asymmetry in the dust (the red and yellow emission to north of the blue disk) suggest that galaxy gas is being stripped by and interspersed into the Virgo ICM by ram pressure. Reproduced with permission from the National Optical Astronomy Obser- vatory/Association of Universities for Research in Astronomy/National Science Foundation. Copyright WIYN Consortium, Inc., all rights reserved.

32 1.7. KEY TESTS IN THIS STUDY

Figure 1.8: Heating by central AGN feedback in the Perseus Cluster, the quintessential cool core galaxy cluster (Fabian et al., 2006). The bright central point source is NGC 1275, the BCG for the Perseus Cluster. The deficits in surface brightness observed to the south and northwest of NGC 1275 are the result of central AGN feedback. More specifically, NGC 1275 is injecting large amounts of energy into the ICM by way of jets, which inflate large cavities in the surrounding ICM. The “inflation” of these cavities results in large amounts of heating in the form of sound waves (observed as ripples in the image) and shock fronts. Reproduced with permission from NASA/CXC/IoA/Andrew Fabian

33 CHAPTER 1. INTRODUCTION

Figure 1.9: The most powerful radio-mode AGN outburst yet observed, in the galaxy cluster MS 0735.6+7421, from McNamara et al. (2005). The blue denotes the locations of X-ray emission as detected by Chandra, while the pink denotes radio emission as detected by the Very Large Array. It is clear from this image that the radio emission resides in “cavities” in the X-ray emitting ICM. Assuming that these cavities are inﬂated by the kinetic energy of the radio-bright jets ejected by the AGN, the AGN jet power has been estimated at 1046 erg s 1 , roughly 3 ∼ − orders of magnitude higher than the X-ray luminosity of the central AGN. Reproduced with permission from NASA/CXC/SAO/Brian McNamara

34 1.7. KEY TESTS IN THIS STUDY

Figure 1.10: The cumulative number counts (logN logS ) of X-ray point sources in the CDFS, − as determined in Lehmer et al. (2012) in four energy bands. The blue, red, and green data points denote the contributions to the total cumulative number counts (in black) from AGN, normal galaxies, and Galactic stars, respectively. In the bottom inset panels for each sub-figure is the fractional contribution of each source class to the total number counts at each flux limit. At most flux limits feasible to a typical Chandra observation (& 10 15 erg cm 2 s 1 in the 0.5 8.0 keV − − − − band), the vast majority of the X-ray point sources are AGN.

35 CHAPTER 1. INTRODUCTION

Figure 1.11: The best ﬁt LDDE model for the XLF from Ueda et al. (2003), which shows the differential comoving number density of AGN at given luminosity, binned by redshift. There is compelling evidence from these curves that the break luminosity and overall normalization both vary with redshift, which suggests that an LDDE model is the appropriate ﬁt to the data.

36 1.7. KEY TESTS IN THIS STUDY

Figure 1.12: The comoving spatial density of luminous AGN as a function of redshift. The model curves were determined by integrating the LDDE XLF model from Ueda et al. (2003) for the three luminosity ranges shown. This ﬁgure shows that the comoving density of the most luminous AGN peaked at z 2, while density of less luminous AGN peaks at lower redshifts. ∼ For this reason, AGN are inferred to form in an “anti-hierarchical” fashion.

37 CHAPTER 1. INTRODUCTION

38 Chapter 2

An Extreme Example of Central AGN Feed- back in MACS J1931.8-2634

2.1 Introduction

This chapter discusses deep multiwavelength observations of a single galaxy cluster, MACS J1931.8- 2634 (Ehlert et al., 2011). MACS J1931.8-2634 is an extreme example of a cluster with a rapidly cooling core, making it an ideal system to test the limits of feedback mechanisms in galaxy clusters. In a short 12 ks Chandra observation of MACS J1931.8-2634 taken in October of 2002 (Allen et al., 2004, 2008), X-ray cavities were detected surrounding a bright central AGN. The physical size of these cavities ( 25 kpc) is similar to those observed in the nearby Perseus Cluster (Fabian ∼ et al., 2003, 2006). Indeed, with its luminous cool core, visible central point source, and very large apparent cooling rate, MACS J1931.8-2634 is in many ways a higher redshift analog to the Perseus Cluster. Deeper observations of MACS J1931.8-2634 were taken with Chandra in August of 2008, increasing the total clean exposure to 100 ks. The combined X-ray data are presented for the ﬁrst ∼ time here, and are complimented with optical (Subaru) and radio (VLA) observations. Our goals are to acquire a better understanding of the thermodynamic structure around the central AGN and the extent to which feedback from the central AGN counteracts this extreme cooling ﬂow. At the redshift of MACS J1931.8-2634 (z = 0.352), 1 arcsec corresponds to 4.926 kpc.

2.2 Chandra Data Reduction and Processing

Two Chandra observations of MACS J1931.8-2634 were performed using the Advanced CCD Imaging Spectrometer (ACIS) in October 2002 and August 2008. The standard level-1 event lists

39 CHAPTER 2. EXTREME AGN FEEDBACK

Table 2.1: Summary of the two Chandra observations of MACS J1931.8-2634. Exposure times are the net exposure after all cleaning and processing as described in Section 2.2.

Obs # Observation Date Detector Exposure Time (ks) 3282 October 20 2002 ACIS-I 10.0 9382 August 21 2008 ACIS-I 89.5 produced by the Chandra pipeline processing were reprocessed using the CIAO (version 4.1.2) software package, including the appropriate gain maps and calibration products (CALDB version 4.1.2). Bad pixels were removed and standard grade selections were applied to the event lists. Both observations were taken in VFAINT mode, and the additional information available in this mode was used to improve the rejection of cosmic ray events. The data were cleaned to remove periods of anomalously high background using the standard energy ranges and binning methods recommended by the Chandra X-ray Center. The net exposure times after processing are summarized in Table 2.1.

2.3 X-ray Imaging Analysis

2.3.1 Surface Brightness Proﬁles

Flat-fielded images were first created in the energy range from 0.7-2.0 keV for each of the Chandra observations. This energy range was chosen to minimize the impact of astrophysical and instrumental background components. All subsequent imaging analysis was performed in this energy range. Surface brightness profiles were produced from these flat-fielded images centered on the central AGN (α(2000) = 19h31m49s.6, δ(2000) = 26h34m33s.6). Each surface brightness profile − was fit with a King plus constant model in the range of 50 to 400 arcsec from the central AGN. The best-fit constant for each observation was subtracted from each surface brightness profile, and Fig. 2.1 shows the exposure-time weighted average of the two surface brightness profiles. Over the radial range of 10-250 arcsec, the profile can be approximately described by a King model with r0 = 6.54 0.23 arcsec and β = 1.35 0.01. ± ± The background level determined from the surface brightness analysis described above was subtracted from each flat-fielded image on a pixel-by-pixel basis. These two images were reprojected into a common aspect solution and combined. This combined image, which is used in all subsequent imaging analysis, is shown in Fig. 2.2.

40 2.3. X-RAY IMAGING ANALYSIS −3 10 ) −2 −4 10 arcsec −1 −5 10 −6 10 −7 10 Surface brightness (cts sec 1 10 100 Radius (arcsec) Figure 2.1: Combined X-ray surface brightness proﬁle for MACS J1931.8-2634 in the energy range 0.7-2.0 keV. The red points denote the total surface brightness, while the black points denote the background subtracted surface brightness.

2.3.2 Background Subtracted, Flat-Fielded Image

On scales larger than 100 kpc, MACS J1931.8-2634 exhibits a general elliptical symmetry, with ∼ the major axis aligned along the approximately north-south direction. There are asymmetries at these scales, however, that can be more readily observed by overlaying the X-ray surface brightness

41 CHAPTER 2. EXTREME AGN FEEDBACK

contours, seen in Fig. 2.2(b). The isophotes are clearly not concentric, and the centroid appears to shift north and south of the central AGN with an amplitude as large as 20-30 kpc. Inside the ∼ central 100 kpc, seen in Fig. 2.2(c), the morphology becomes considerably more complex. The bright central point source is surrounded by two bright ridges to the north and south. The northern ridge is brighter and has a relatively sharp boundary to the north, while the southern ridge is trailed by diffuse emission extending further to the south. These features, as well as the varying isophote centroids on large scales are indicative of past oscillations of the core, which currently appears to be moving to the north.

2.3.3 Substructure Analysis

Bandpass Filtering

In order to better resolve small scale structures around the center of the cluster, a high-frequency bandpass ﬁlter was applied to the image shown in Fig. 2.2(a). The functional form of the ﬁlter is given as

k 2 k F(k) = 0 (2.1) ³ ´ 2 1 + k k0 ³ ´ We have set the scale length k0 = 10 arcsec. The transformed image is shown in Fig. 2.3(a), and shows the central AGN as well as the ridges to the north and south more clearly. Other features more apparent in this image are depressions in the X-ray brightness immediately to the east and west of the central AGN. Nearby systems show clear cavities in the X-ray brightness near the central AGN, similar in shape to these (e.g. Fabian et al., 2003; Bˆırzan et al., 2004). Unlike the cavities in those systems, however, there is no evidence in Fig. 2.3(a) as to where the outer boundary of these cavities might lie.

Two-Dimensional Surface Brightness Modeling

The X-ray image of Fig. 2.2(a) was also fit with a two-dimensional elliptical beta model. The center of the model (x0, y0) was fixed and the ellipticity ǫ and the position angle, θ, were free parameters in the fit. Since the image has already been background subtracted, no further considerations for the background were included in the fit. The best fit parameters for this model are r0 = 3.21 ± 0.04 arcsec , ǫ = 0.290 0.004, θ = 4.11 0.46 degrees and α = 1.139 0.004. ± ± ± 42 2.3. X-RAY IMAGING ANALYSIS

(a)

(b) (c)

Figure 2.2: Combined, background subtracted, ﬂat-ﬁelded image of MACS J1931.8-2634 in the energy range of 0.7-2.0 keV. a) The central 5.7 5.7 of MACS J1931.8-2634. b) Same as ′ × ′ in (a), but zoomed in by a factor of about 2 and overlaid with logarithmic surface brightness contours in blue. The centroids of these contours shift north and south of the central AGN by distances with an amplitude of up to 20-30 kpc.43 c) Same as in (a) but zoomed in by a factor ∼ of about 8 to focus on the central AGN, the bright ridges about 25 kpc to the north and south, and the diffuse emission extending further to the south. CHAPTER 2. EXTREME AGN FEEDBACK

(a) (b)

Figure 2.3: Images of MACS J1931.8-2634 that emphasize substructure. a) Image of MACS J1931.8-2634 after applying a high-frequency bandpass ﬁlter. This image better shows the central AGN and bright ridges to the north and south. Depressions in the X-ray emission also arise to the east and west of the central AGN. b) Image of MACS J1931.8-2634 after subtracting the best-ﬁt elliptical-β continuum model and adaptively smoothing the residuals. This image better shows a large scale ( 200 kpc) spiral feature wrapping around the center of the cluster. ∼

The ﬁt was then subtracted from the image, and the residuals adaptively smoothed. The smoothed residuals image is shown in Fig. 2.3(b). With the elliptical model subtracted, a spiral pattern begin- ning just east of the central AGN emerges. Such a pattern is expected to arise in off-axis mergers (e.g. Ascasibar & Markevitch, 2006).

2.4 Image Deprojection Analysis

An image deprojection analysis of MACS J1931.8-2634 was undertaken following the manner described in Allen et al. (2008) (see also Schmidt & Allen, 2007). In brief, the azimuthally averaged X-ray surface brightness profile (centered on the central AGN) and the deprojected temperature profile are combined to simultaneously determine the X-ray emitting gas mass and total mass profiles of the cluster. We assume that the dark+luminous mass distribution follows the NFW model of Navarro, Frenk & White (1995, 1997)

44 2.5. SPECTRAL ANALYSIS

For a given scale radius and concentration parameter, a model temperature profile can be calculated and compared with the observed deprojected temperature profile. The observed deprojected temperature profile was azimuthally averaged and binned into annular regions with roughly 5,000 counts in each (see Section 2.5). The mass profile parameters were stepped over a range of values to determine best-fit values and uncertainties using a χ2 minimization technique. This analysis assumed hydrostatic equilibrium and spherical symmetry, both of which are obviously not true in the vicinity of the central AGN. To account for these assumptions, the data for the central 50 kpc have been statistically down-weighted by adding 30% systematic uncertainties to the measured temperatures. = +0.05 Our best fit NFW profile has a scale radius of rs 0.26 0.02 Mpc, a concentration param- = +0.40 − eter of c 6.25 0.75, and an equivalent velocity dispersion (Allen, Schmidt & Fabian, 2002) − = √ = +60 1 σ 50rscH(z) 966 18 km s− . The integrated mass profile is shown in Fig. 2.4(a). The −= +19 = +0.31 14 enclosed mass within r2500 505 3 kpc is M2500 2.64 0.06 10 M . − − × ⊙ The bolometric luminosity, cooling time, and equivalent mass deposition rate profiles have also been determined using the calculations described in detail in White, Jones & Forman (1997). These are shown in Figs. 2.4(b), 2.4(c), and 2.4(d) respectively. These profiles show that there is rapid cooling within the central 50 kpc. Within this region and in the absence of of balancing heat sources, 1 a cooling flow with an equivalent mass deposition rate of M˙ 700 M yr− would be expected. The ∼ ⊙ bolometric luminosity within this region is 1 1045 erg s 1, and the cooling time is less than 1 Gyr. ∼ × −

2.5 Spectral Analysis

2.5.1 Methods

The Chandra observations of MACS J1931.8-2634 are sufﬁciently deep for high signal-to-noise spectra to be extracted from relatively small regions. This enables us to carry out detailed, spatially resolved measurements of the thermodynamic quantities of the ICM. All spectral analysis was carried out using XSPEC (Arnaud, 2004, version 12.5). The backgrounds for all spectral analysis were extracted directly from the science observations, speciﬁcally from a region roughly the same distance from the center of the detector as the cluster, but on the diagonally opposite chip. The background regions are devoid of point sources and cluster emission.

45 CHAPTER 2. EXTREME AGN FEEDBACK 14 10 ) ) −1 . O erg s 44 13 10 20 40 Enclosed Mass (M 12 Luminosity (10 10

0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 Radius (Mpc) Radius (Mpc) (a) (b) ) 10 −1 yr . O 1.5×10 10 10 9 Cooling Time (yr) Equiv. Mass Deposition Rate (M 0 5×10 0 500 1000 0 0.05 0.1 0.15 0.2 0 0.05 0.1 0.15 0.2 Radius (Mpc) Radius (Mpc) (c) (d)

Figure 2.4: Best fit profile from the image deprojection analysis. a) The best fit integrated gravitational mass profile in black, with the 1σ confidence interval shown as the red curves. b) The integrated bolometric luminosity profile. c) The cooling time profile. d) The equivalent mass deposition rate profile. The high luminosity within the central 50 kpc leads to very short cooling times in this region and very large nominal mass deposition rates. For comparative pur- 1 poses, the nominal mass deposition rate in the Perseus Cluster is roughly 400 M yr− (Fabian ∼ ⊙ et al., 2002).

46 2.5. SPECTRAL ANALYSIS

Regions of Interest

The spectral structure of MACS J1931.8-2634 was measured in both one-dimensional radial profiles and two-dimensional maps. In addition, the spectra of several regions of interest identified from the imaging analysis (specifically the central AGN and bright central northern and southern ridges) were also investigated in detail. To account for contamination by the central AGN, the 2.5 arcsec radius region surrounding the central AGN was excluded from the spectral profiles and maps. Region specific response matrices and ancillary response files were created for all spectra. Spatially resolved spectral maps for the cluster were extracted in regions determined by the contour binning algorithm of Sanders (2006), which creates bins of equivalent signal-to-noise, following contours in surface brightness. Regions were selected to have a signal-to-noise of 30, resulting in approximately 1, 000 counts per bin. Azimuthally averaged spectral profiles were measured in annular regions with nearly equal numbers of counts. Initially radial profiles were made using annular bins that each contain roughly 3,000 counts. Measuring the metallicity requires data with a higher signal-to-noise, so we also carried out a similar analysis using annular bins with roughly 10,000 counts each. The chosen center for all annuli was the location of the central AGN. The spectral properties of each annular region were measured both in projection and deprojection. Deprojection was implemented using the PROJCT mixing model in XSPEC.

Modeling the Emission

All spectral regions were initially modeled as a single temperature optically thin plasma using the MEKAL code of Kaastra & Mewe (1993) incorporating the Fe-L calculations of Liedahl, Osterheld & Goldstein (1995) and the photoelectric absorption models of Balucinska-Church & McCammon (1992). We used the determinations of solar element abundances given by Anders & Grevesse (1989). The abundances of metals (Z) were assumed to vary with a common ratio with respect to the Solar values. The single-temperature plasma model has three free parameters: the temperature (kT), the metallicity (Z), and normalization (K). In each region, the spectral analysis assumed a fixed Galactic absorption column of 8.3 × 20 2 10 cm− (Kalberla et al., 2005), consistent with the value measured directly from the X-ray spectra. The modified Cash statistic in XSPEC (Cash, 1979; Arnaud, 2004) was minimized to determine the best fit model parameters and uncertainties. All uncertainties given are 68% (∆C = 1) confidence intervals, unless otherwise noted. In the analysis of the bright ridges to the north and south of

47 CHAPTER 2. EXTREME AGN FEEDBACK

the AGN, the spectroscopic model included an additional MKCFLOW component, appropriate for a scenario where gas is assumed to cool at constant pressure from an upper temperature down to a lower temperature. The upper temperature and metallicity of the MKCFLOW component were tied to their corresponding MEKAL components, and the low temperature was ﬁxed to 0.1 keV.

Thermodynamic Quantities

Several thermodynamic quantities can be calculated directly from the best ﬁt MEKAL model parameters. The electron density (ne), pressure (P), and entropy (S ) of the ICM are derived from the MEKAL temperature (kT) and normalization (K) as

4π 1014 (1 + z)2 D2 K n2 = × A (2.2) e 1.2V where the cosmological value of the angular diameter distance DA is 1016 Mpc at the cluster redshift. The volume of the region, V, is given in units of cm3. If the region had not been deprojected (in the case of the thermodynamic maps), the volume of the region was estimated as

= 3 Ω 2 2 V DA θmax θmin (2.3) q − where θmax,min are the maximum and minimum angular distances of any point in the region to the center of the cluster, respectively, and Ω is the solid-angle extent of the region in the sky (Henry, Finoguenov & Briel, 2004; Mahdavi et al., 2005). If the spectrum had been fully deprojected (true for the annular proﬁles), the volume was calculated as

4 V = πD3 θ3 θ3 (2.4) 3 A max min ³ − ´ Since the uncertainties on the temperature are considerably larger than those on the density (> 10% for the temperature as compared to 2-5% for the densities), the fractional uncertainties on the ∼ pressure and entropy given are similar to the corresponding temperature measurement.

2.5.2 Spectral Results

Thermodynamic Mapping

The maps of temperature, density, pressure, and entropy in MACS J1931.8-2634 are shown in Fig. 2.5. One of the most notable features is the spiral of low temperature gas wrapping to the east and

48 2.5. SPECTRAL ANALYSIS

north of the central AGN seen in the large scale temperature map. This is spatially coincident with the surface brightness excess seen in Fig. 2.3(b). Such spirals can be due to either ram pressure stripping of an infalling subcluster core, or merger induced oscillatory motion of the cluster core. Both scenarios are seen in many nearby systems and hydrodynamic simulations (e.g. Churazov et al., 2003; Ascasibar & Markevitch, 2006; Dupke, White & Bregman, 2007; Lagana, Andrade- Santos & Lima Neto, 2009; Owers et al., 2009; ZuHone, Markevitch & Johnson, 2009; Million et al., 2010a). Zooming in on the temperature and entropy structure surrounding the AGN, Figs. 2.6(a) and 2.6(b) show that there are also two small regions of higher temperature to the east and west of the central AGN spatially coincident with the depressions in X-ray emission noted in Fig. 2.3(a). Al- though the temperature of these regions is up to 2 keV higher than their surroundings, the statistical signiﬁcance is modest. The lowest temperature and entropy gas is located 25-30 kpc to the north of the AGN and spatially coincident with the X-ray bright northern ridge. There is a similar ridge to the south which also has lower temperature and entropy gas, but at lower signiﬁcance.

Spectroscopic Cooling in the Bright Northern and Southern Ridges

Since cooling flows are only expected in the inner, densest regions of the ICM, we have searched for the presence of spectroscopic cooling in the bright ridges to the north and south of the central AGN. The regions defined as the northern and southern ridges are shown in Fig. 2.7. These regions were constructed to surround the brightest emission in the image shown in Fig. 2.2(c). The two ridges are approximately equal distances from the central AGN, but have different spectral properties. The northern ridge has an emission weighted temperature of kT = 4.78 0.64 ± and a metallicity Z = 0.53 0.11Z . This metallicity is higher than the average of this cluster of ± ⊙ Z = 0.36 0.03Z , discussed in more detail in Section 2.5.2. Adding a cooling flow component ± ⊙ to the spectral model of the northern ridge, we measure a spectroscopic mass deposition rate of 1 M˙ = 165 56 M yr− . Comparing the luminosity of the composite MEKAL+MKCFLOW model ± ⊙ with the MKCFLOW component alone, the spectroscopic cooling of gas down to kT 0.1 keV ∼ contributes 30% of the emission from the northern ridge region. The southern bright ridge has ∼ = +1.30 = a similar overall temperature (emission weighted kT 5.87 0.46), but a lower metallicity (Z − 1 0.22 0.09) and a 90% confidence upper limit on the spectral cooling rate of M˙ < 83 M yr− . ± ⊙ These results are summarized in Table 2.2. We note that the MEKAL+MKCFLOW model offered a significant improvement to the spectral fit of the northern ridge (∆C 10 with one additional fit parameter, a significant improvement at the ∼ 49 CHAPTER 2. EXTREME AGN FEEDBACK

200 kpc Temperature 32:00.0 200 kpc

30.0

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(a) (b)

Pressure Entropy 200 kpc 200 kpc

0.001 0.0099 0.037 0.082 0.14 0.23 0.32 0.44 0.58 0.73 0.9 15 21 30 44 67 104 161 252 398 627 990

(c) (d)

Figure 2.5: Thermodynamic maps for MACS J1931.8-2634. a) The surface brightness image of Fig. 2.2(a) with the ﬁeld of view of the thermodynamic maps overlaid in blue. b) Temper- 3 2 ature, kT, in units of keV. c) Pressure, in units of keV cm− . d) Entropy, in units of keV cm . The 1σ fractional uncertainties in the mapped quantities are < 20 per cent. The white region at the center of these maps is the exclusion region for the central∼ AGN. There are a total of 60 independent regions shown in these maps, which have a ﬁeld of view approximately 200 arcsec (1 Mpc) in diameter.

50 2.5. SPECTRAL ANALYSIS

Temperature 50 kpc Entropy 50 kpc

3.5 4.2 4.9 5.6 6.3 7 7.7 8.4 9.1 9.8 10 13 16 21 30 43 67 104 167 271 441 721

(a) (b)

Figure 2.6: The thermodynamic structure around the central AGN. These maps are identical to those shown in Fig. 2.5, but zoomed in on the central AGN by approximately a factor of 4. a) Temperature, in keV. b) Entropy, in units of keV cm2. It is clear in both of these maps that the lowest temperature and entropy gas is located approximately 25 kpc to the north of the central AGN.

99.9% confidence level), almost identical to the ∆C improvement obtained with a two temperature ∼ (MEKAL+MEKAL ) fit, which introduces two additional fit parameters to the default MEKAL model. We also attempted to further resolve the spectroscopic cooling flow by using 2 or more MKCFLOW components with different temperature ranges, but none of these more complicated models provided a further statistically significant improvement to the fit. For the southern ridge, the fit was not improved by the addition of either a cooling flow or a second MEKAL component.

Azimuthally Averaged Thermodynamic Proﬁles

The azimuthally averaged temperature proﬁle for MACS J1931.8-2634 is shown in Figure 2.8. It is clear in this proﬁle that the temperature does not decrease monotonically towards the center as is expected in a cool core cluster (e.g. Allen et al., 2001; Vikhlinin et al., 2005), but instead appears to increase in the central-most regions. This central jump in temperature is most clear in deprojection, where the innermost temperature is several keV higher than the measured temperature

51 CHAPTER 2. EXTREME AGN FEEDBACK

Table 2.2: Spectroscopic measurements of the X-ray bright ridges to the north and south of the central AGN. Temperatures are given in keV, metallicities are in solar units, and the cooling 1 ﬂow rate is given in M yr− . Upper limits are given at the 90% conﬁdence level. ⊙

Measurement North South +1.30 kT 4.78 0.64 5.87 0.46 ± − Z 0.53 0.11 0.22 0.09 ± ± ˙ +45 M 165 67 < 83 −

in the adjacent region. Such a sharp discontinuity in the temperature is often associated with shock heating, but the presence of the cooler ridges separated from the central AGN makes presence of shock heating ambiguous. To account for these cool ridge substructures, thermodynamic profiles were taken with the ridge regions excluded. These thermodynamic profiles are shown in Fig. 2.9. These profiles more accurately describe the average thermodynamic structure of the ICM. After excluding the ridges, the deprojected temperature in the central-most region still does not decrease towards the center. The sharp temperature jump seen in the center of the deprojected temperature profile is less pronounced, but still present, and there are weak indications of discontinuities in the central bin of the density profile as well. All of the thermodynamic profiles of Figure 2.9 are consistent with shock heating occurring in the central 20 kpc, but the small number of regions and relatively low signal-to-noise do not allow for us to claim an unambiguous detection. Such a jump in the central temperature could also be due to the presence of cavities devoid of ICM gas. In that case, the measured central temperature would be due to ICM gas in front of and behind the central AGN. This should lead to a flatter deprojected density profile in the central regions, a feature that is not observed. The innermost annular region of the thermodynamic profiles is also considerably larger than any apparent cavities in the X-ray emission, so it is unlikely that this temperature jump is due to projection effects involving cavities. Both the temperature and density profiles show other discontinuities within the central 100 kpc. The density profile decreases discontinuously at several locations while the temperature profile increases sharply at r 70 kpc. These profiles lead to clear discontinuities in the entropy and an ∼ unusually flat pressure profile. Such discontinuities arise from the presence of either cold fronts or weak ( < 2) shocks. Since the lower temperature gas on the inner edges of these fronts M 52 2.6. OPTICAL STRUCTURE OF THE CLUSTER CORE

Table 2.3: Summary of the optical data. All images were taken with SuprimeCam at the Subaru telescope. The quoted seeing values are those of the coadded images. Filter Exposure time [s] Observation date Seeing B 1680 2006-06-25 0′′. 89 V 1636 2006-05-30 0′′. 83 R 3360 2006-06-25 0′′. 76 I 2400 2006-05-30 0′′. 88 z 1620 2007-07-18 0′′. 71 has a higher density, this proﬁle is more consistent with the presence of cold fronts than shock heating. Heating from weak shocks cannot be ruled out, however, since the state of the ICM at earlier times before any bulk motion or shock heating is unknown. Deeper data are required to distinguish between these possibilities.

Metallicity Proﬁles

Surprisingly, the metallicity profile of MACS J1931.8-2634 shown in Fig. 2.10 exhibits no deviations from a constant metallicity of Z = 0.36Z out to distances as large as 400 kpc. All attempts ⊙ to fit the data to a multi-temperature MEKAL model did not lead to any significant increases in the central metallicity. A central metallicity peak is almost always observed in the profiles for cool core clusters (Allen & Fabian, 1998; De Grandi & Molendi, 2001; Leccardi & Molendi, 2008; Leccardi, Rossetti & Molendi, 2010; Ehlert & Ulmer, 2009). The only region in the cluster that has a significant metallicity enhancement is the bright ridge of X-ray gas to the north of the central AGN, where the metallicity is closer to Z = 0.5Z . The unusual metallicity profile of MACS J1931.8- ⊙ 2634 argues that the cool core may have undergone substantial stripping, likely associated with its bulk motion. A dramatic example of stripping of a cool core due to bulk motion has been recently reported in the nearby Ophiuchus Cluster (Million et al., 2010a).

2.6 Optical structure of the cluster core

We observed MACS J1931.8-2634 in broad-band BVRIz ﬁlters with SuprimeCam on Subaru (Ta- ble 2.3). The data were reduced with a dedicated weak lensing and photometry pipeline based on the GABODS pipeline of Erben et al. (2005) as part of a larger cluster sample (von der Linden et al., in preparation). Figure 2.11(a) shows a BRz three-color image of the central 2.6 2.6 of the cluster. ′ × ′ 53 CHAPTER 2. EXTREME AGN FEEDBACK

10.0 50 kpc

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55.0 51.5 51.0 50.5 19:31:50.0 49.5 49.0 48.5 48.0

0 2 6 14 25 39 56 76 99 126 155

Figure 2.7: The regions deﬁned as the bright northern and southern ridges, drawn over the image of Fig. 2.2(c) in blue. These encapsulate the regions of the brightest X-ray emission in the cluster apart from the central AGN. The spectral properties of these regions are discussed in detail in Section 2.5.2.

Similar to the X-ray structure, the optical morphology of the cluster core exhibits clear structure in the North-South direction: the Brightest Cluster Galaxy (BCG) and the intracluster light (ICL) are highly elongated in this direction. In Fig. 2.11(b) we highlight the extent of the ICL. The SuprimeCam images are very deep, and we have taken significant care in flat-fielding the images (von der Linden et al., 2012). This allows us to trace the ICL to 28.3 mag arcsec 2. Correcting for ∼ − 2 cosmological surface brightness dimming, this would correspond to 27 mag arcsec− at z = 0. North of the BCG, two stars mask the ICL, but there is evidence that the ICL extends slightly northwest of the stellar halos. To the south, the ICL appears to encompass the second brightest galaxy, located & 200 kpc south of the BCG. At these extremes, the detected ICL extends to 200 kpc north and south ∼ of the BCG, but only to 70 kpc east and west. The ICL of MACS J1931.8-2634 is thus highly ∼ elongated, with an axis ratio of 0.3. At radii & 50 kpc, the ICL traces the overall gravitational ∼ potential and is tied to the evolution history of the cluster (e.g. Kelson et al., 2002; Napolitano et al., 2003; Gonzalez, Zabludoff & Zaritsky, 2005). The stars in the ICL are collisionless, like the dark matter, and thus the shape of the ICL should presumably reflect the shape of the core of the cluster dark matter halo. The elongation of the dark matter distribution indicates the direction of the last

54 2.6. OPTICAL STRUCTURE OF THE CLUSTER CORE 10 Temperature (keV) 2 5 1020 50 100 200 500 Radius (kpc) Figure 2.8: The azimuthally averaged temperature profile of MACS J1931.8-2634. The red triangles denote the projected temperature profile, while the black squares denote the deprojected profile. The central region has a much higher temperature than the adjacent regions, which suggests that shock heating might be present. The radius of the temperature minimum corresponds to the distance of the northern and southern ridges.

merger (e.g. Roettiger, Loken & Burns, 1997), and thus MACS J1931.8-2634 likely experienced a merger within the N-S direction. The timescale of this event is not clear, since the elongation persists for several Gyr (Roettiger, Loken & Burns, 1997; Moore et al., 2004). A confirmation of this elongation of the dark matter halo by gravitational lensing is currently not possible - the ground- based data do not reveal any strong lensing features. Furthermore, the sightline to MACS J1931.8- 2634 is close to the galactic center (l = 12.5669 , b = 20.09 ), and the field is crowded with ◦ − ◦ stars, which prohibit weak lensing shape measurements for an appreciable number of background galaxies. Of particular interest is also the structure of the BCG. Fig. 2.12(a) shows the central 30 arcsec × 30 arcsec of the SuprimeCam BRz image. We see ‘pink’ and ‘blue’ filaments extending to the northwest and southeast, respectively. The ‘pink’ nebulosity to the northwest of the BCG signals emission in the blue (B) and red (z) bands. At the redshift of the cluster, the emission lines Hα, [NII], and [SII] are redshifted into the z band. The response of the CCD is not uniform across the filter due to the decreasing quantum efficiency at longer wavelengths, and Hα falls onto the most sensitive wavelength interval of the filter throughput. In order to single out the line emission, we subtract the adjacent band (I) from the z-band, scaling the I-band image such as to remove the large-scale light

55 CHAPTER 2. EXTREME AGN FEEDBACK

distribution from the dominant stellar population of the BCG. We identify the resulting emission as predominantly Hα line emission, although this needs to be confirmed spectroscopically. We also subtract the I-band from the B and R-bands, in order to better visualize emission not stemming from the underlying, old stellar population visible in the I-band. The resulting image is shown in Fig. 2.12(b). The dominant feature here is a bright filament to the northwest, of both Hα emission and blue emission. At the cluster redshift, the [OII] doublet is shifted redward of the B filter, and thus the most likely interpretation of blue light is continuum emission from young stars. To the southeast, a filament of bright blue emission is visible, which lacks strong Hα emission. Faint loops of Hα and blue light are also visible to the east and west. Overall, the structure is reminiscent of NGC 1275, the central galaxy of the Perseus cluster, which exhibits a rich system of Hα filaments, as well as filaments of young stars (Conselice, Gallagher & Wyse, 2001; Fabian et al., 2008a). From the data at hand, however, it appears that in MACS J1931.8-2634, Hα filaments are accompanied by blue filaments (Hα alone would be visible as deep red, not as pink, in our images). The reverse is not always true, however: we do not detect Hα emission coincident with the blue filament to the southeast. For comparison, this is significantly different from NGC 1275, where most Hα emission is not accompanied by star formation (Canning et al., 2010).

In Fig. 2.12(c) we overplot contours from the X-ray emission from Fig. 2.2(b). We see that the brightest knots of the northwestern filament (which are bright both in Hα and blue light) coincide with the northern ridge identified in the X-ray imaging and thermodynamic mapping (Sect. 2.5.2). The southwestern filament also could be associated with the southern ridge. The X-ray cavities, on the other hand, are at almost 90◦. angles to the bright filaments.

In Fig. 2.12(d) we overplot contours from the radio emission (Sect. 2.7). The brighter radio emission seems to coincide with the brightest Hα emission, whereas the fainter radio emission is elongated in an East-West direction, following the X-ray cavities.

Finally, in Fig. 2.12(e) and (f) we show the HST WFPC2 snapshot exposure of MACS J1931.8- 2634 (1200s, F606W ﬁlter). This allows us to see details at the very core of the BCG, which are unresolved in the deeper SuprimeCam images. Apart from the central AGN point source, we see three bright knots to the north, along spiral-shaped ﬁlaments.

Giant Hα ﬁlaments and on-going star formation, as evident by the presence of young, blue stars, are a common occurrence in the BCGs of cool-core clusters (Allen, 1995; Crawford et al., 1999; Hatch, Crawford & Fabian, 2007; Rafferty, McNamara & Nulsen, 2008a). In the most Hα luminous systems, the Hα luminosity scales well with the star formation rate (Allen, 1995; Crawford et al.,

56 2.7. RADIO OBSERVATIONS WITH VLA

1999; O’Dea et al., 2008). However, in general, star formation is not the only source of photo- ionizing radiation in BCGs, as is evidenced by Hα emission present even in the absence of young stars, the spatial offset of Hα filaments and young star clusters observed in some near-by systems (Crawford, Sanders & Fabian, 2005; Canning et al., 2010), and the higher [NII]/Hα line ratios when young stars are not present (Hatch, Crawford & Fabian, 2007). In the BCG of MACS J1931.8-2634, young stars are clearly associated with the Hα emission. The Hα emission is furthermore coincident with the “northern ridge” defined in Fig. 2.7. Assuming ratios of [NII]/Hα=0.7 and [SII]/Hα=0.3 (typical of Hα-luminous BCGs, Crawford et al., 1999), the Hα luminosity in this region is L(Hα) ∼ 9 2 1042h 2 erg s 1. This would make it the most Hα-luminous BCG known. Crawford et al. ± × 70− − furthermore find that the typical color excess in Hα luminous BCGs is E(B V) 0.3, albeit − ∼ with large scatter. Assuming Milky-Way type extinction, the standard Kennicutt (1998a) conversion between L(Hα) and star formation rate (which fits Hα luminous BCGs well, again with large scatter, 1 Crawford et al., 1999; O’Dea et al., 2008) thus suggests SFR 170M yr− . This is in remarkable ∼ ⊙ 1 agreement with the X-ray mass deposition rate of the northern ridge ( 165 60M yr− ). However, ∼ ± ⊙ one needs to keep in mind that the SFR value is only a rough estimate, using only broad-band imaging, and assuming average values of quantities with large observed scatter.

2.7 Radio Observations with VLA

1.4 GHz radio observations were made with the Very Large Array (VLA) of the National Radio Astronomy Observatory on 2006 April 14. The data were obtained in A configuration, and the time on source was 54 minutes. A central radio source with flux density 70 mJy is clearly associated ∼ with the central AGN and the core of the elliptical galaxy. Approximately 45” to the south is a Narrow Angle Tail (NAT) radio galaxy with flux density 135 mJy. As shown in Fig. 2.13(a), ∼ the tails of the NAT are swept back to the south, in the same north-south orientation as the major axis of the central elliptical. The radio morphology of the central AGN (seen in Fig. 2.13(b)) is amorphous without the clearly defined jets or lobes that are found in many radio galaxies. Such amorphous radio structures have been seen associated with cD galaxies in cooling core clusters such as PKS0745-191 (Baum & O’Dea, 1991; Taylor, Barton & Ge, 1994), 3C317 in A2052 (Zhao et al., 1993), and PKS 1246-410 in the Centaurus cluster (Taylor, Fabian & Allen, 2002). Most likely the radio jets have been disrupted on small scales by dense gas.

57 CHAPTER 2. EXTREME AGN FEEDBACK

2.8 The Central AGN: Power and Accretion

The energy of the outburst occurring at the central AGN manifests itself as both radiative emission and as a jet that inﬂates cavities ﬁlled with radio plasma. The energy being input into both of these channels can be estimated from the observations.

2.8.1 Estimating the Radiative Power

The central AGN is sufficiently bright in X-rays to directly measure its spectrum and luminosity. Since its spectrum is presumed to be non-thermal in origin, we have modeled it with a variety of power law models. Source counts are extracted from a 2 arcsec region centered on the AGN. The spectral background is extracted from regions that are chosen to be near the AGN and avoid the bright ridges to the north and south, which are likely poor representations of the true background surrounding the AGN. The source and background regions are shown in Fig. 2.14(a). The net spectrum for the AGN using these regions is shown in Fig 2.14(b). Three different assumptions about the absorption have been examined: 1) that the absorption is due only to Galactic contributions, and is fixed at the value of Kalberla et al. (2005); 2) that the absorption is only due to Galactic contributions, but the column density nH is a free parameter in the fit; and 3) that the absorption includes both a fixed Galactic component and an intrinsic component at the redshift of the cluster with the intrinsic column density nH a free parameter. The results of these fits including the model fluxes and luminosities between 0.7 and 8.0 keV are given in Table 2.4. It is clear from the fit statistics that a larger absorption column density is favored (∆C > 12, a significant improvement at a confidence level well above 99.9%). We conclude that the central AGN has a luminosity in the energy range of 0.7-8.0 keV of 8 1043 erg s 1. Systematic uncertainties arising from the choice of the background region are ∼ × − estimated at 30%. ∼

2.8.2 Estimating the Jet Power

The radio and X-ray emission both suggest that X-ray cavities filled with radio plasma reside to the east and west of the central AGN. The energy and power required to inflate and fill these cavities (4PV) can be estimated using the methods described by Allen et al. (2006), (see also Dunn & Fabian, 2004; Dunn, Fabian & Taylor, 2005; Churazov et al., 2002). Neither the X-ray nor radio image provide an well-defined, unambiguous choice for cavity

58 2.8. THE CENTRAL AGN: POWER AND ACCRETION

regions. The X-ray image shows two regions to the east and west of the central AGN that look like cavities, although the outer boundaries are difficult to determine, particularly given the presence of the bright ridges to the north and south. In nearby systems, depressions in the X-ray emission associated with the full extent of the radio plasma-filled cavities are typically visible only in very deep X-ray observations (Bˆırzan et al., 2008). The radio emission, on the other hand, has a much more naturally defined outer boundary, but our 1.4 GHz radio data for this z = 0.35 cluster do not show clear lobe or jet structure to the radio source. The boundaries of this radio emission suggest that it is confined by the surrounding X-ray gas, but we caution that if the radio plasma were ‘leaking out beyond’ the boundary of the cavities then estimating cavity volumes based on the radio emission would lead to overestimating the energy in the cavities (Finoguenov et al., 2008). With these uncertainties in mind, identical calculations were performed with two different sets of cavities: one set based on the structure of the radio emission (hereafter the radio cavities) and another set based on the apparent cavities in the X-ray images (hereafter the minimal X-ray cavities). The true cavity volumes, 4PV enthalpy, and jet power are expected to lie somewhere between the values calculated from these two sets of cavities, both shown in Fig. 2.15.

Both the radio and minimal X-ray cavities were modeled as tri-axial ellipsoids with volume

V = (4/3)πrlrwrd. The measured lengths rl and rw are the lengths of the axes in the image plane along and perpendicular to the jet axis, respectively. The ﬁnal length, rd, is the axis of the cavity along the line of sight. The initial length of the axis along the line of sight was estimated as the smaller of the two planar axis lengths, but allowed to vary independently from them. Calculating the sound speed for the X-ray emitting gas with mean molecular weight µ = 0.62 and adiabatic index

γ = 5/3, we estimated the time scale for bubble formation as tage = (rl/cs). From this, we calculated the power required to inﬂate the cavities as roughly Pjet = (4PV/tage). A Monte Carlo analysis was performed that drew the temperature, density, and all three spatial axes from independent Gaussian distributions. The assumed uncertainties on rl, rw, and rd were 20%, 30%, and 30%, respectively, leading to a systematic uncertainty of 50% in the volume. Both the temperature and density were ∼ calculated from the major axis rl using a power-law parameterization of the proﬁles shown in Fig. α 2.9 between 8 and 80 kpc (X(r) = X0r , X kT, ne). From these variables the pressure, enthalpy, ∈ and jet power were then calculated. The prior assumptions and subsequent calculations of the cavity energetics are listed in Table 2.5. Although each 4PV calculation includes systematic uncertainties of the particular cavity volume, the dominant uncertainty is identifying a particular set of cavities, which leads to an uncertainty in the 4PV enthalpy of approximately a factor of 8 and jet power uncertain within a factor of roughly 4.

59 CHAPTER 2. EXTREME AGN FEEDBACK

Table 2.4: Spectral models for non-thermal emission of the central AGN. The three models are 22 2 described in Section 2.8.1. The absorption column densities are given in units of 10 cm− and all fluxes and luminosities are given in the energy range of 0.7-8.0 keVand take into account 13 2 1 their respective absorption. The units for the flux are 10− erg cm− s− , and the units for 43 1 luminosity are 10 erg s− . Errors listed are 1σ confidence levels. The final column provides the C-statistic and degrees of freedom for the best fit model.

Model NH,Gal NH,Int Γ Norm FX LX C/ν 5 +0.14 0.58 Fixed Gal. Abs. 0.083 0.0 1.24 0.07 (2.05 0.15) 10− 1.76 0.11 7.27 0.44 519.81/497 ± ± × − − +0.64 5 +0.18 +0.73 Free Gal. Abs. 0.42 0.11 0.0 1.70 0.16 (3.86 0.96) 10− 1.97 0.39 8.14 1.63 505.68/496 ± ± − × − − +0.27 +0.88 5 +0.21 +0.89 Free Int. Abs. 0.083 0.71 0.22 1.70 0.16 (3.83 0.68) 10− 1.95 0.23 8.05 0.97 506.42/496 − ± − × − −

We find that the total 4PV enthalpy between the two bubbles, after accounting for all uncertainties, is approximately 1 8 1060 erg. This corresponds to a power input into the ICM from the − × 45 1 jet of approximately Pjet 4 – 14 10 erg s . The mechanical energy going into inflating these ∼ × − cavities is nearly two orders of magnitude larger than the radiative emission of the AGN, and larger than the bolometric luminosity within the central 50 kpc of the cluster. Based on the scaling rela- 44 tion of Cavagnolo et al. (2010) and the radio luminosity, the inferred jet power is Pjet = 7.7 10 × 1 erg s− , consistent with the lower range of jet powers measured here after accounting for the scatter. The temperature profile shows evidence for heating that goes out to roughly the same distance as the radio emission, so it is possible that the bubbles are in fact as large as the radio emission even though the X-ray depressions are much smaller in scale. The jet power derived from the radio cavities is comparable to the power input measured in the 200 kpc cavities of MS0735.6+7421 (McNamara et al., 2005), which was previously the system with the most powerful jets measured.

60 2.8. THE CENTRAL AGN: POWER AND ACCRETION

Table 2.5: Calculations of the enthalpy and jet power in MACS J1931.8-2634 for the cavities shown in Fig. 2.15. These calculations follow the procedure described in Section 2.8.2 and are also discussed in Allen et al. (2006). The ﬁrst seven rows are the prior assumptions going into the calculation while the ﬁnal six rows are derived quantities.

Parameter East (Radio) West (Radio) East (X-Ray) West (X-Ray)

rl ( kpc) 20.2 4.0 30.0 6.0 13.4 2.7 12.9 2.6 r ( kpc) 25.9 ± 9.1 24.5 ± 7.3 9.4 ±2.8 10.3 ± 3.1 w ± ± ± ± rd ( kpc) 20.2 6.1 24.5 7.3 9.4 2.8 10.3 3.1 kT ( keV) 5.30 ±0.25 5.30 ±0.25 5.30 ± 0.25 5.30 ±0.25 0 ± ± ± ± αkT 0 0 0 0 3 ne,0 ( cm− ) 3.47 0.28 3.47 0.28 3.47 0.28 3.47 0.28 α 1.17± 0.02 1.17± 0.02 1.17± 0.02 1.17± 0.02 ne − ± − ± − ± − ± 69 3 +0.83 +1.31 V (10 cm− ) 1.25 0.50 1.64 0.65 0.11 0.08 0.14 0.08 − − ± ± 3 +0.20 +0.11 P ( keV cm− ) 0.50 0.10 0.33 0.06 0.85 0.20 0.81 0.25 − − ± ± 60 +2.65 4PV (10 erg) 4.62 2.16 3.98 1.51 0.64 0.32 0.85 0.40 ± − ± ± 1 c (kms− ) 1170 30 1170 30 1170 30 1170 30 s ± ± ± ± t (106 yr) 17.0 3.4 25.2 5.02 11.4 2.1 10.7 2.1 age ± ± ± ± 45 1 P (10 ergs− ) 8.50 4.61 5.36 3.06 1.90 1.22 2.42 1.31 jet ± ± ± ±

61 CHAPTER 2. EXTREME AGN FEEDBACK 10 ) −3 0.02 0.05 0.01 0.1 Density (cm 5 −3 Temperature (keV) 5×10 −3 2×10 1020 50 100 200 500 1020 50 100 200 500 Radius (kpc) Radius (kpc) (a) (b) ) ) 2 −3 0.1 100 Entropy (keV cm Pressure (keV cm 50 200 500 0.02 0.05 0.2 0.5 1020 50 100 200 500 1020 50 100 200 500 Radius (kpc) Radius (kpc) (c) (d)

Figure 2.9: Azimuthally averaged thermodynamic profiles for MACS J1931.8-2634 with the bright northern and southern ridges excluded. a) Projected (red triangles) and deprojected (black squares) temperature profiles. b) Deprojected density profile. c) Deprojected pressure profile. d) Deprojected entropy profile. There are clear discontinuities in both the temperature and density profiles at distances of r 70 kpc and further discontinuities in the density profile, ∼ the origin of which may be either bulk motion of cold fronts or weak shock heating.

62 2.8. THE CENTRAL AGN: POWER AND ACCRETION

LEC Profile Deprojected Projected Metallicity (Solar) 0.2 0.4 0.6

0 200 400 Radius (kpc) Figure 2.10: Azimuthally averaged metallicity profiles of MACS J1931.8-2634. The black curve is the mean profile derived for the Low Entropy Core (LEC) sample from Leccardi, Ros- setti & Molendi (2010) scaled to the estimated r180 for MACS J1931.8-2634. The blue circles are the projected metallicity profile, while the red triangles are the deprojected metallicity profile.

63 CHAPTER 2. EXTREME AGN FEEDBACK

Figure 2.11: (a): BRz image of the central 2.6 2.6 (780kpc 780kpc) of MACS J1931.8-2634. ′ × ′ × The sightline to MACS J1931.8-2634 is close to the galactic center, and thus most objects in this image are foreground stars. The box indicates the ﬁeld of Fig. 2.12. (b): The deepest band, R, smoothed with a 0′′. 6 Gaussian kernel, and scaled to bring out low-surface brightness 2 features. The black contour follows a surface brightness level of 28.3 mag arcsec− . Bright stars are marked as white circles. For the two stars north of the BCG, red circles indicate where the PSF surface brightness reaches 28.3 mag arcsec 2. Note how the BCG envelope and the ∼ − intra-cluster light are highly elongated in the N-S direction.

64 2.8. THE CENTRAL AGN: POWER AND ACCRETION

Figure 2.12: Optical structure of the BCG of MACS J1931.8-2634. (a): SuprimeCam BRz image of the central 30 arcsec 30 arcsec. (b): For this image, the contribution from the old × stellar population of the BCG (as traced by the SuprimeCam I-band image) was subtracted from each of the B,R and z images before combining them to a color image. This enhances the blue and pink features visible to the southeast and northwest of the central AGN. “Pink” signals contributions from predominantly the blue (B) and the red (z) channel. At the redshift of the cluster, the Hα line falls into the z-band, and thus this emission likely stems from Hα nebulosity surrounding MACS J1931.8-2634. The blue emission, on the other hand, likely signals a young stellar population. Interestingly, the Hα emission and young stars coincide in the northwestern region, whereas in the southeast Hα emission is absent, or significantly weaker. (c): Contours of the X-ray surface brightness map overlaid on the image in (b). The brightest knots in the northwestern filament coincide with the peak of the cluster X-ray emission north of the AGN point source. Thermodynamic mapping shows that this is also the coolest, densest part of the ICM, i.e. the cool core. A second peak (which also corresponds to cold, dense gas) is also seen close to the southwestern filament. The X-ray cavities, on the other hand, are located at 90deg ∼ angles to the bright filaments. (d): Overlay of the radio emission on (b). (e): The HST WFPC2 snapshot, showing the same field of view as a). (f): The central 7.5 arcsec 7.5 arcsec of the × HST snapshot. Note the bright knots in a spiral-like structure emanating to the Northwest of the central, brightest knot.

65 CHAPTER 2. EXTREME AGN FEEDBACK

33:00.0 10.0 50 kpc 200 kpc 15.0 30.0 20.0

-26:34:00.0 25.0

-26:34:30.0 30.0 35.0

40.0 35:00.0

45.0

30.0 50.0

55.0 56.0 54.0 52.0 19:31:50.0 48.0 46.0 44.0 51.5 51.0 50.5 19:31:50.0 49.5 49.0 48.5 48.0

0 2 6 14 25 39 56 76 99 126 155 0 2 6 14 25 39 56 76 99 126 155

(a) (b)

Figure 2.13: 1.4 GHz radio emission in MACS J1931.8-2634 observed with the VLA. a) X-ray image from Fig. 2.2(b) overlaid with radio contours in magenta. The NAT galaxy to south of the center of the cluster is the source of the brightest radio emission in this cluster. b) X-ray image from Fig. 2.2(c) with the radio contours overlaid in magenta. This ﬁgure shows the amorphous structure of the central radio source, which is clearly centered on the X-ray bright AGN. The radio contours are logarithmically spaced between 9 10 5 and 0.11Jy/beam. The × − beam size for this observation is 1.25 arcsec 2.78 arcsec, and the position angle of the beam × ellipse is 5.34 degrees.

66 2.8. THE CENTRAL AGN: POWER AND ACCRETION

10.0 50 kpc

15.0

20.0

25.0

-26:34:30.0

35.0

40.0

45.0

50.0

55.0 51.5 51.0 50.5 19:31:50.0 49.5 49.0 48.5 48.0

0 2 6 14 25 39 56 76 99 126 155

Figure 2.14: Determining the radiative power emitted from the central AGN. a) The region of spectral extraction for the AGN (in blue) and background (magenta). b) The spectrum of the AGN with the best-fit power-law model including a fixed Galactic absorption and a free intrinsic absorption. The residuals of the fit are shown in the panel below.

10.0 50 kpc

15.0

20.0

25.0

-26:34:30.0

35.0

40.0

45.0

50.0

55.0 51.5 51.0 50.5 19:31:50.0 49.5 49.0 48.5 48.0

0 2 6 14 25 39 56 76 99 126 155

Figure 2.15: Estimating the jet power using the X-ray cavities and radio emission. The image of Fig. 2.3(a) is shown with the radio contours overlaid in magenta, the radio cavities overlaid in blue, and the minimal X-ray cavities overlaid in black. The determination of the 4PV enthalpy and jet power are derived from a Monte Carlo analysis that generates plausible elliptical cavities based on these ellipses. The true cavity volumes are expected to reside between the volumes calculated from these two sets of cavities.

67 CHAPTER 2. EXTREME AGN FEEDBACK

68 Chapter 3

The Ubiquity of Galaxy Stripping by Ram Pressure in the Neighborhood of M86

3.1 Introduction

This chapter discusses recent results regarding the ubiquity of ram pressure stripping in a region of 1 surrounding the massive elliptical galaxy M86 in the Virgo Cluster (Ehlert et al., 2013). This ∼ ◦ work presented an investigation into the thermodynamic structure spanning the entire region across four galaxies in the Virgo Cluster, utilizing the full information of ﬁve separate XMM-Newton observations including new observations of M84. We measured temperature and metallicity structure around these galaxies and investigated how interactions of these galaxies with each other and the ICM have transformed their gas reservoirs. Observations of the Virgo Cluster have shown that it is composed of three separate sub-groups: one centered on M87, the Brightest Cluster Galaxy (BCG); one centered on M86, an elliptical galaxy roughly one degree ( 300 kpc) to the west of M87; and the ﬁnal sub-group centered on ∼ NGC 4472 (also known as M49) approximately 4.5◦ to the south of M87 (Binggeli, Tammann & Sandage, 1987; Binggeli, Popescu & Tammann, 1993; Bohringer¨ et al., 1994). Spectroscopic studies of M86 have shown that it has an absolute blue-shifted velocity of 250 km s 1 while M87 ∼ − has a red-shifted velocity of 1300 km s 1. Given an average temperature of the ambient Virgo ∼ − ICM of 2.3 keV (Urban et al., 2011, corresponding to a sound speed of 700 km s 1), M86 must ∼ ∼ − be traversing through the Virgo Cluster supersonically with a Mach number of at least & 2. M In the vicinity of M86, additional galaxies are located within approximately 100 kpc of one ∼ another in projection. The two most massive galaxies, M86 and M84, are separated by a projected distance of only 60 kpc. Although these galaxies appear similar in optical imaging studies, further studies utilizing optical spectroscopy and X-ray observations show that they are interacting with the

69 CHAPTER 3. THE UBIQUITY OF RAM PRESSURE STRIPPING

ambient Virgo ICM in different manners. Observations of M86 with Chandra and XMM-Newton (Finoguenov et al., 2004; Randall et al., 2008) show that it is undergoing signiﬁcant ram pressure stripping, with diffuse X-ray emitting gas as well as cooler material trailing behind the galaxy for distances of approximately 150 kpc in projection to the northwest. There is only sparse evidence ∼ for ram pressure stripping in observations of M84 with Chandra (Finoguenov et al., 2008), but there is clear evidence for a powerful AGN outburst at the center of this galaxy(Finoguenov et al., 2008). M84 has an absolute red-shifted velocity of 1000 km s 1 (Smith et al., 2000; Trager et al., 2000), ∼ − approximately consistent with the measured velocity for M87. The region in between M84 and M86 is also claimed to be the site of bright intracluster light emission (Rudick et al., 2010). Two other galaxies are detected in the X-rays nearby: NGC 4388 and NGC 4438. These spiral galaxies 1 1 have absolute redshifted velocities of 2566 km s− (NGC 4388, Rines & Geller, 2008) and 71 km s− (NGC 4438, Kenney et al., 1995), respectively. We assume a nominal distance of 17 Mpc to the Virgo Cluster, at which 1 arcsecond corresponds to 82 pc, and 1 arcminute to 4.92 kpc.

3.2 Data Processing and Analysis Methods

A total of five separate XMM-Newton pointings were analyzed, the details of which are listed in Table 3.1. The data products were cleaned and processed using the XMM-Newton Science Analysis System (SAS) (version 11.0), and in particular the Extended Source Analysis Software (ESAS, Snowden et al., 2008) package included with SAS. We have utilized events from the two MOS and PN detectors for this study. Calibration products and event lists were first produced using the standard SAS routines. Good time intervals (GTI’s) for each event list were determined using the MOS-FILTER and PN-FILTER routines. Four of the five pointings had little time contaminated by flares, but the most recent observation (of M84, Observation # 0673310101, PI S. Ehlert) was heavily contaminated by flaring, with only 40 ks of the original 130 ks MOS exposure deemed ∼ suitable for analysis. No further evidence for flaring was observed in any of the event lists after GTI filtering.

3.2.1 Imaging Analysis

Background-subtracted, exposure corrected images of all ﬁve pointings were produced in the soft energy band, 0.3 1.0 keV. The backgrounds for imaging analysis were determined from customized − blank-sky observations (Carter & Read, 2007) and closed-ﬁlter wheel observations to account for

70 3.2. DATA PROCESSING AND ANALYSIS METHODS

20’ = 98.4 kpc N

20:00.0 E 10:00.0

NGC 4438 M86

13:00:00.0 M84 50:00.0

NGC 4388 40:00.0

28:00.0 30.0 27:00.0 30.0 12:26:00.0 30.0 25:00.0 24:30.0

0.000010 0.000020 0.000062 0.000226 0.000885 0.003488

Figure 3.1: Background-subtracted, exposure corrected mosaic of ﬁve XMM-Newton point- 1 ings surrounding M86 (in units of cts s− ), utilizing the MOS+PN detectors in the energy band of 0.3 1.0 keV. The four X-ray brightest Virgo Cluster members observed in this mosaic − (M86, M84, NGC4388, and NGC 4438) are labeled. Diffuse gas is observed trailing to the northwest of M86, to the east of M86 in the direction of NGC 4438, to the south of M84, and to the east of NGC 4388.

71 CHAPTER 3. THE UBIQUITY OF RAM PRESSURE STRIPPING

Table 3.1: Summary of the ﬁve XMM-Newton observations utilized in this study. Exposure times are the net exposure after all cleaning and processing as described in Section 3.2 for each detector. The M84 observation (Obs # 0673310101, denoted by an asterisk) was heavily contaminated by ﬂares. The original event list was split into two separate event lists for each MOS instrument, the summed exposure time of which is shown below.

Obs # Target Obs. Date MOS1 (ks) MOS2 (ks) PN (ks) 0108260201 M86 Core July 01 2002 62.44 63.53 43.68 0110930701 NGC 4388 December 12 2002 8.46 10.05 3.84 0210270101 M86/ NGC 4438 December 19 2004 25.42 25.25 23.79 0210270201 M86 Tail December 27 2004 20.38 19.92 19.02 0673310101* M84 June 01 2011 37.14 42.96 15.42 the instrumental background. We use blank sky files with similar pointing directions and Galactic absorption column densities to M84 and M86. The closed-filter wheel counts images were subtracted from both the blank-sky and science exposures after renormalizing to match the total counts in each of these images outside of the field-of-view. The remaining blank sky emission was then subtracted from the science images. We also accounted for out-of-time (OOT) events in the PN detector images for each pointing using standard procedures.

3.2.2 Thermodynamic Mapping

Our XMM-Newton observations are sufficiently deep for high signal-to-noise spectra to be extracted from relatively small regions. This enables us to carry out detailed, spatially resolved measurements of the thermodynamic quantities across the entire mosaic. All spectral analysis was carried out using XSPEC (Arnaud, 2004, version 12.6). Instrumental background was determined for the source spectra using closed filter wheel observations of the detectors renormalized to have the same count rate in the 10 12 keV energy band. We subtracted the out-of-time events from the PN spectra − before performing these fits. We modeled the sky background for each spectral bin using the background model of Urban et al. (2011), renormalized to account for the appropriate region geome- tries. This model assumes a sum of three components: an absorbed power law from the unresolved point sources (De Luca & Molendi, 2004), absorbed thermal emission with kT 0.2 keV from the ∼ Galactic halo (Kuntz & Snowden, 2000) and the unabsorbed thermal emission from the Local Hot Bubble (Sidher et al., 1996; Kuntz & Snowden, 2000). Tests show that no additional background components are necessary. Allowing the temperatures, photon indexes, or normalizations of the

72 3.2. DATA PROCESSING AND ANALYSIS METHODS

N 20’=98.4 kpc

E

NGC 4438

M86

M84

NGC 4388

920 921 924 929 936 945 956 969 984 1001 1020

Figure 3.2: An r-band image of the region surrounding M86 from the Sloan Digital Sky Survey. The X-ray surface brightness contours are overlaid in white, which show the stripped X-ray emitting gas trailing these galaxies. The stripped gas tail is especially pronounced for the M86 at the center of the image. Tails of stripped gas are observed to the south of M84 and to the northwest of M86. Diffuse soft emission is detected trailing eastward from M86 in the direction of NGC 4438. Faint, diffuse emission is also detected at the position of the edge-on spiral galaxy NGC 4402, located 10 to the north of M86. ∼ ′

73 CHAPTER 3. THE UBIQUITY OF RAM PRESSURE STRIPPING

background components to vary did not change the results in a signiﬁcant manner.

Regions of Interest

We identified the independent regions of interest for our spectral analysis by binning the background subtracted image into regions of equal signal to noise. Here we made use of the Weighted Voronoi Tessellations binning algorithm by Diehl & Statler (2006), which is a generalization of the Voronoi binning algorithm of Cappellari & Copin (2003). We binned the image using two signal-to-noise thresholds (hereafter the low S/N and high S/N maps, respectively). Spectra were extracted from each spectral region, and appropriate response files were generated using the RMFGEN and ARF- GEN tools. All spectral fits were performed in the energy band 0.4 7.0 keV, although we ignored − the energy range 1.2 1.8 keV in order to avoid the instrumental Al and Si emission lines in the − MOS detectors. In the resulting energy band, there are roughly 4000 counts per region in our low ∼ S/N maps and 7000 counts per region for our high S/N maps. ∼

Spectral Modeling and Thermodynamic Quantities

For the low S/N regions, we fit the spectra with an absorbed, single temperature APEC plasma model. We utilized the photoelectric absorption model of Balucinska-Church & McCammon (1992) and fixed the Galactic absorption column density to the value derived by Kalberla et al. (2005) 20 2 NH = 2.13 10 cm . We assume the solar element abundance ratios given by Grevesse & Sauval × − (1998). The temperature, metallicity, and the normalization were all independent free parameters. Best fit model parameters were obtained by minimizing the modified C-statistic in XSPEC. All uncertainties quoted are 68% (∆C = 1) confidence intervals. The high S/N spectra were fitted with a similar model, but with an additional thermal component to account for the ambient Virgo Cluster emission viewed in projection. The temperature of this thermal emission was fixed to 2.3 keV, in agreement with measurements of the diffuse Virgo ICM at the same distance from M87 (Urban et al., 2011). The metallicities of the two thermal components were linked to be the same. The exact choice of the temperature of the ambient Virgo ICM does not have a strong influence on the measured temperature or metallicity for the second component. To search for multi-temperature structure in the high S/N map, we also attempted to fit the spectra with a two-temperature plus cooling flow model (APEC+APEC+MKCFLOW). For the cooling flow component we set the upper and lower temperatures to 1 keV and 0.1 keV, respectively. All of our measured temperatures have an uncertainty of less than 10%, regardless of the model. ∼ 74 3.3. RESULTS

3.3 Results

The background subtracted, exposure corrected image of the region surrounding M86 is shown in Figure 3.1. To the north-west of M86 a long tail of diffuse gas is observed, which has been discussed in detail by Finoguenov et al. (2004) and Randall et al. (2008). The length of this tail is 150 kpc in projection, although given the large line-of-sight velocity of M86 with respect to the ∼ ambient Virgo ICM this is likely a conservative lower limit to its true length. The width of this tail in projection, however, is roughly uniform along its entire length. To the east of M86, diffuse emission is observed out to the neighboring galaxy NGC 4438. Diffuse gas is also detected for a distance of approximately 15 kpc to the south of M84. These tails of X-ray emission trailing M86 and M84 ∼ extend beyond the bulk of the optical galaxy emission, as shown in the Sloan Digital Sky Survey image of this ﬁeld (Figure 3.2). There is no evidence for diffuse emission that bridges between M84 and M86. There is also tentative evidence for diffuse X-ray emission trailing to the east of NGC 4388.

3.3.1 Temperature Structure

Maps of the temperature and metallicity structure across the region are shown in Figure 3.3. Cool gas with a temperature of 1 keV is detected in various directions around M86, with the the major- ∼ ity of this gas phase located in the regions immediately surrounding the galaxy and to the north-west of the galaxy. This 1 keV component traces the morphology of M86’s X-ray tail, and originates ∼ from the ram pressure stripping of M86 (e.g. Forman et al., 1979; Fabian, Schwarz & Forman, 1980; Nulsen, 1982; White et al., 1991; Finoguenov et al., 2004; Randall et al., 2008). To the east of M86 is a site of cooler X-ray emitting gas, observed to have a temperature of 0.6 keV, spatially coincident with the brighter diffuse X-ray emission spanning between M86 ∼ and NGC 4438. Gas with a temperature of 0.6 keV is also observed just to the north of M86, ∼ extending to the northwest in the same direction as the 1 keV component. Fitting each spectral ∼ bin in the region between M86 and NGC 4438 with a two-temperature plus cooling flow model results in no detection of significant cooling flows. For any particular bin, these fits constrain the 1 spectroscopic cooling rate of X-ray gas from 1.0 keV to 0.1 keV to . 0.01M yr− , and the upper ⊙ limit for the integrated spectroscopic cooling rate across all of the bins between these two galaxies is 1 . 0.1M yr− . These temperature measurements are consistent with those determined by Chandra ⊙ observations of NGC 4438 (Machacek, Jones & Forman, 2004). In the vicinity of M84, we detect the presence of cool ( 0.7 keV) gas trailing to the south of ∼ 75 CHAPTER 3. THE UBIQUITY OF RAM PRESSURE STRIPPING

the galaxy, out to a distance of 15 kpc. This is signiﬁcantly larger than the scale of AGN feedback ∼ in this system (see Figure 3.4). The temperature of this gas is observed to increase smoothly to the south, until it becomes indistinguishable from the ambient ICM. The presence of cool gas trailing to the south of M84 suggests ram pressure stripping of its X-ray halo amidst northward motion. We use the presence of this tail to provide an order-of-magnitude estimate of the rate at which X-ray gas is stripped from M84. In order to determine the gas mass in the tail, we assume it has a prolate ellipsoidal geometry with a semi-major axis of 7.5 kpc and a semi-minor axis of 4.2 kpc (shown in Figures 3.4). This geometry is based on the extent to which we can detect cooler ( 1 keV) ∼ gas to the south of the cavity regions. We only include the region to the south of the AGN-driven cavities seen in Chandra observations of M84. Based on the surface brightness enhancement in this region, we estimate that the density of the gas in the tail is 0.005 cm 3, roughly a factor of 2 ∼ − denser than the surrounding Virgo ICM (Urban et al., 2011). With these assumptions, we determine that a mass of 7 107 M of X-ray gas has been stripped by the bulk motion of M84 through the ∼ × ⊙ Virgo ICM. Using the sound crossing time as an estimate for the time scale over which this stripping 1 occurred and an assumed sound speed of 700 km s− , the overall mass stripping rate is calculated to 1 be 7M yr− . Since there is no evidence for jumps in the ICM temperature or surface brightness ∼ ⊙ indicative of a shock front to the north of M84, this galaxy is likely traversing through the ICM at sub-sonic speeds1. Resultingly, the mass stripping rate we estimate is likely an upper limit as to the true rate at which M84’s X-ray halo gas is being stripped by ram pressure. To the west of M86 in the direction of M84, a clear temperature gradient is observed over the entire region. Between the two galaxies is a 20 kpc band of emission consistent with the ambient ∼ Virgo ICM, in both one-temperature and two-temperature ﬁts.

3.3.2 Metallicity Structure

Although the single-temperature and two-temperature fits qualitatively agree in their metallicity measurements (see Figures 3.3b and 3.3d), we focus our discussion on the measurements from the two-temperature fits. These measurements are performed at higher signal-to-noise and are less sensitive to biases in metallicity measurements that arise by modeling multi-phase emission with a single temperature (Buote, 2002). For comparative purposes, the metallicity of the Virgo cluster at the same radius from M87 (Urban et al., 2011) is approximately Z 0.3 Z . ∼ ⊙ 1Since M84 and M87 have similar line-of-sight velocities, it is also unlikely that M84 has a significant velocity component through the Virgo ICM along the line-of-sight. This maximizes the likelihood that such a shock front, if present, would be detected.

76 3.3. RESULTS

The metal-richest gas is located near the center of M86, which has a measured metallicity of at least 1Z . Metal-rich gas (& 0.5Z ) is observed in the direction of M86’s stripped tail, out to ∼ ⊙ ⊙ distances of 90 100 kpc. There is no strong evidence for metal-rich gas spanning between M86 ∼ − and NGC 4438, consistent with the metallicity observed in Chandra observations of NGC 4438 (Machacek, Jones & Forman, 2004). However, it is possible that a metallicity-measurement bias arising from unaccounted temperature phases is still present here. A smaller enrichment in metals (Z & 0.5Z ) is observed near the core of M84. ⊙

3.3.3 Ultraviolet Observations with GALEX

We have also utilized publicly available ultraviolet (UV) observations of the region between M86 and NGC 4438 with GALEX in order to search for star formation in this region. We used observation ID “NGA Virgo MOS10” (observation date 2004-03-11), part of the GALEX Ultraviolet Virgo Cluster Survey (Boselli et al., 2011), which is well centered between the two galaxies. There are no siginficant detections of young stellar populations in the far UV image of this field. Convert- ing the observed surface brightness of far-UV photons into a star formation rate using Kennicutt (1998b), we place a strict upper limit on the star formation rate in the region of the Hα filaments of 1 . 0.1M yr− . ⊙

77 CHAPTER 3. THE UBIQUITY OF RAM PRESSURE STRIPPING

Figure 3.3: Temperature and metallicity maps of the 1 region surrounding M86, including ∼ ◦ the galaxies M84, NGC 4388, and NGC 4438. The upper row shows our low S/N ( 4000 ∼ counts per bin) temperature and metallicity maps. The values show our best-fit parameters using a single-temperature thermal emission model. The lower row shows temperature and metallicity maps from our high S/N spectra ( 7000 counts per bin). The values in these maps ∼ are derived from a two-temperature fit, with the second temperature fixed to 2.3 keV and the metallicities of both components tied to the same value. This two-temperature fit optimally identifies regions hosting gas significantly cooler than the ambient Virgo Cluster gas, which has an assumed temperature of 2.3 keV. The inclusion of an additional temperature component also reduces metallicity measurement biases.

78 3.3. RESULTS

3’ = 15 kpc 3’ = 15 kpc

0.000015 0.000022 0.000048 0.000153 0.000574 0.0022390.5 0.65 0.7 0.77 0.86 1.2 1.5 1.7 1.8 2.1 2.3

Figure 3.4: A zoomed in view of M84 with XMM-Newton. Both of these images show the same field of view. The dashed ellipse denotes the region which we use to estimate the rate at which X-ray halo gas is being stripped from M84. Left: The background subtracted, exposure corrected image of M84 in the 0.3 1.0 keV energy band. The black contours are derived − from Chandra observations of this galaxy, which show the extent of the AGN-driven cavities (Finoguenov et al., 2008). Right: The corresponding temperature map of this same region, in units of keV. The bin map is similar to that described in §3.2.2, although with a only 250 ∼ net counts per bin in order to resolve smaller scale features. Each bin is fit with a single- temperature plasma model with Galactic absorption (PHABS APEC). The same Chandra × contours are overlaid in black. The brighter gas trailing to the south of M84 is shown to be significantly cooler than the ambient ICM.

79 CHAPTER 3. THE UBIQUITY OF RAM PRESSURE STRIPPING

8’=40 kpc

NGC 4438 M86

0.000000 0.000003 0.000020 0.000115 0.000638 0.003491

Figure 3.5: The correlation between the cool X-ray gas and Hα emission detected by Kenney et al. 2008. (a) Temperature map of Figure 3.3d zoomed in to emphasize the temperature structure surrounding M86 and overlaid with contours of Hα imaging. There is a clear correlation of the Hα emission with the coolest gas phases in both the eastern and northern plumes extending out from M86. The centers of M86 and NGC 4438 are denoted by the cyan crosses. (b) The X-ray surface brightness image of Figure 3.1, zoomed in to the same region as (a). The Hα image contours are overlaid in cyan.

80 Chapter 4

The Population of X-ray AGN in Galaxy Clusters: X-ray Point Source Catalog Pro- duction

4.1 Introduction

This chapter discusses the analysis procedure at the heart of this work: the production of X-ray point source catalogs in the vicinity of galaxy clusters, the ﬁrst results of which can be found in ? Unlike the previous chapters of this thesis that focus on observations of one or a handful of galaxies in clusters, the analysis discussed in this chapter and the next two is a survey of the entire population of X-ray point sources detected in Chandra observations of 135 galaxy clusters. The superlative point-spread function (PSF) and relatively low background of Chandra make it by far the most sensitive instrument for point source surveys. The procedures and algorithms employed for this study build upon the most recent analysis of the (Xue et al., 2011), which currently has a total exposure time of 4 Ms. The results of Chapters 5 and 6 will utilize calculations with the catalogs and sensitivity maps discussed in this chapter and provide a number of new measurements regarding the population of X-ray AGN in galaxy clusters.

The structure of this chapter is as follows: The ﬁrst section will discuss the cluster sample and X-ray telescope data included in this study, and subsequent sections will address the details of the procedure utilized to produce a rigorously selected catalog of X-ray point sources for each cluster. At the end of this chapter, the procedure for quantifying the selection function (also known as the sensitivity map) for this survey is discussed.

81 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

4.2 The Cluster Sample

The clusters included in our study have been drawn from three wide-area cluster surveys derived from the ROSAT All Sky Survey (RASS; Truemper, 1993): the ROSAT Brightest Cluster Sam- ple (BCS; Ebeling et al., 1998); the ROSAT-ESO Flux-Limited X-ray Sample (REFLEX; Bohringer¨ et al., 2004); and the MAssive Cluster Survey (MACS; Ebeling et al., 2007, 2010). We also included clusters from the smaller but deeper 400-Square Degree ROSAT PSPC Galaxy Cluster Survey (hereafter the smaller but deeper 400 deg2 survey, Burenin et al., 2007). Each sample covers a distinct volume of the Universe: BCS covers the northern sky at z < 0.3; REFLEX covers the southern sky 2 at z < 0.3; and MACS covers higher redshifts, 0.3 < z < 0.7, at declinations > 40◦. The 400 deg survey covers high galactic latitudes at redshifts of z < 1. The galaxy clusters included in these samples have been instrumental in recent cosmological studies (for a review see Vikhlinin et al., 2009; Mantz et al., 2010a,b; Allen, Evrard & Mantz, 2011). All of the clusters we choose from these samples have at least 10 ks of exposure time with Chandra available in public archives as well as robust measurements of the cluster masses and virial radii (Mantz et al., 2010a,b). In total, 135 unique galaxy clusters are included, with redshifts ranging from 0.1 < z < 0.9. General information for the clusters and the Chandra data sets used may be found in Table 4.1. These clusters are among the most massive and X-ray luminous clusters in the Universe, and therefore host high numbers of galaxies and large masses of ICM gas. We therefore expect the inﬂuences of the local cluster environment to be pronounced in this sample. With measurements of r500 available for each cluster are able to relate observed trends in the AGN population to the virial radii of each cluster.

Mass measurements and the associated overdensity radii, r500, for each cluster are taken from

Mantz et al. (2010a,b). The typical uncertainties in measurements of r500 are of order 10%. The ∼ r500 values and X-ray centroids for the clusters are summarized in Table 4.1.

4.3 Initial X-Ray Data Processing

All of the galaxy cluster ﬁelds were observed with the Advanced CCD Imaging Spectrometer (ACIS) aboard Chandra. The majority of the observations utilize the ACIS-I chip array, but we also include observations utilizing ACIS-S. For ACIS-S observations, we only utilize chips S1, S2, and S3, for which the most accurate calibration products are available. The standard level-1 event lists produced by the Chandra pipeline processing were reprocessed using the CIAO (version 4.3) software package, including the appropriate gain maps and calibration products (CALDB version

82 4.4. CANDIDATE SOURCE CATALOGS

4.4.6). Bad pixels were removed and standard grade selections were applied. The data were cleaned to remove periods of anomalously high background, using the standard energy ranges and binning methods recommended by the Chandra X-ray Center. The net exposure times after processing are summarized in Table 4.1. Images for each cluster observation were produced in each of three energy bands, hereafter designated the full band (0.5 8.0 keV), the soft band (0.5 2.0 keV), and the hard − − band (2.0 8.0 keV). − For each observation, an exposure map was produced in each energy band, calculating the product of quantum efficiency and effective area across each field of view. Each exposure map was spectrally weighted over its respective energy band, assuming a photon index of Γ = 1.4 1 and a hydrogen column density fixed to the value measured in the Leiden-Argentine-Bonn survey (LAB, Kalberla et al., 2005). Effective exposure time maps were calculated for each observation and energy band by dividing the exposure map by its maximum effective area and multiplying by the exposure time, similar to the procedure discussed in Hornschemeier et al. (2001). In instances where there was more than one Chandra observation of a cluster, images and exposure maps were created for each observation individually, and then reprojected into a common aspect solution and combined into a single coadded cluster image/exposure map. These coadded images and exposure maps only include active CCD chips from the primary field of view for each observation (i.e. chips I0-I3 for ACIS-I observations, and chips S1-S3 for ACIS-S observations).

4.4 Candidate Source Catalogs

An initial catalog of candidate sources was produced for each cluster using the WAVDETECT algorithm (Freeman et al., 2002) and the full-band (0.5 8.0 keV) image. Our WAVDETECT runs − utilized a “ √2 sequence” of wavelet scales (i.e. 1, √2, 2, 2 √2, 4, 4 √2, 8, 8 √2, and 16 pixels) and a 5 false-positive probability threshold of 10− . This false-positive threshold is more liberal in including faint sources than that commonly used in other studies and allows for recovering legitimate sources that fall below formally stricter thresholds (e.g. Alexander et al., 2001; Xue et al., 2011). The trade- off, however, is that we incur an appreciable number of spurious sources in our initial catalogs, many of which are associated with structures in the cluster ICM. Where all observations of a given cluster utilized a common detector (e.g. all observations were performed with ACIS-I), we utilized

1For this study, the canonical AGN source was chosen to have a photon index of Γ = 1.4, to remain consistent with other studies of AGN populations in the ﬁeld, in particular the CDFS (e.g. Xue et al., 2011; Lehmer et al., 2012).

83 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Table 4.1. Summary of the cluster sample and Chandra observations listed in order of cluster redshift.

Name z RA DEC OBS ID # Obs Date Detector Exposure (ks) M500 r500 NH Abell 2163b 0.203 243.941 6.148 1653 2001-06-16 ACIS-I 71.14 38.48 2.223 15.40 Abell 2163b 0.203 243.941 −6.148 545 2000-07-29 ACIS-I 9.44 38.48 2.223 15.40 Abell 520b 0.203 73.537− 2.921 4215 2003-12-04 ACIS-I 66.27 11.95 1.505 5.65 Abell 520b 0.203 73.537 2.921 528 2000-10-10 ACIS-I 9.46 11.95 1.505 5.65 Abell 520b 0.203 73.537 2.921 7703 2007-01-01 ACIS-I 5.08 11.95 1.505 5.65 Abell 209bc 0.206 22.971 13.613 3579 2003-08-03 ACIS-I 9.98 12.62 1.531 1.44 Abell 209bc 0.206 22.971 −13.613 522 2000-09-09 ACIS-I 9.96 12.62 1.531 1.44 Abell 963bc 0.206 154.264− 39.047 7704 2007-02-18 ACIS-I 5.06 6.82 1.247 1.25 Abell 963bc 0.206 154.264 39.047 903 2000-10-11 ACIS-S 36.28 6.82 1.247 1.25 RXJ0439.0+0520a 0.208 69.759 5.345 527 2000-08-29 ACIS-I 9.59 2.74 0.918 8.92 RXJ0439.0+0520a 0.208 69.759 5.345 9369 2007-11-12 ACIS-I 19.86 2.74 0.918 8.92 RXJ0439.0+0520a 0.208 69.759 5.345 9761 2007-11-15 ACIS-I 8.65 2.74 0.918 8.92 Abell 1423b 0.213 179.322 33.611 538 2000-07-07 ACIS-I 9.87 8.72 1.346 1.81 Zwicky 2701 0.214 148.204 51.884 3195 2001-11-04 ACIS-S 26.91 4.00 1.039 0.75 RXJ1504.1-0248a 0.215 226.031 2.804 4935 2004-01-07 ACIS-I 13.29 10.95 1.456 5.97 RXJ1504.1-0248a 0.215 226.031 −2.804 5793 2005-03-20 ACIS-I 39.15 10.95 1.456 5.97 Abell 773b 0.217 139.469− 51.726 3588 2003-01-25 ACIS-I 9.39 8.55 1.340 1.28 Abell 773b 0.217 139.469 51.726 5006 2004-01-21 ACIS-I 19.81 8.55 1.340 1.28 Abell 773b 0.217 139.469 51.726 533 2000-09-05 ACIS-I 11.25 8.55 1.340 1.28 RXJ0304.1-3656b 0.219 46.013 36.941 9413 2008-03-16 ACIS-I 19.86 4.43 1.073 2.30 RXJ0237.4-2630b 0.222 39.364 −26.507 9412 2008-03-03 ACIS-I 18.38 5.64 1.162 1.82 Abell 2261bc 0.224 260.612− 32.132 5007 2004-01-14 ACIS-I 24.31 14.42 1.588 3.19 Abell 2261bc 0.224 260.612 32.132 550 1999-12-11 ACIS-I 9.06 14.42 1.588 3.19 Abell 1682b 0.226 196.711 46.558 3244 2002-10-19 ACIS-I 9.77 12.35 1.501 1.04 Abell 2667a 0.226 357.915 26.083 2214 2001-06-19 ACIS-S 9.64 8.97 1.356 1.73 RXJ0638.7-5358b 0.227 99.697 −53.974 9420 2008-04-11 ACIS-I 19.89 10.31 1.421 6.06 Abell 1763b 0.228 203.829− 40.999 3591 2003-08-28 ACIS-I 19.59 16.96 1.673 0.82 RXJ0220.9-3829b 0.228 35.235 38.481 9411 2008-02-29 ACIS-I 19.86 4.42 1.066 1.96 Abell 2219bc 0.228 250.084− 46.708 896 2000-03-31 ACIS-S 42.29 18.87 1.737 1.76 Abell 2111b 0.229 234.921 34.418 544 2000-03-22 ACIS-I 10.29 7.75 1.285 1.84 Z5247 0.229 188.592 9.784 539 2000-03-23 ACIS-I 9.28 8.20 1.306 1.61 Abell 2390ac 0.233 328.404 17.695 4193 2003-09-11 ACIS-S 95.06 15.15 1.613 6.21 Abell 2390ac 0.233 328.404 17.695 500 2000-10-08 ACIS-S 9.82 15.15 1.613 6.21 Abell 2390ac 0.233 328.404 17.695 501 1999-11-05 ACIS-S 9.04 15.15 1.613 6.21 Z2089 0.235 135.153 20.894 7897 2006-12-23 ACIS-I 9.04 3.07 0.946 2.86 RXJ2129.6+0005ac 0.235 322.415 .088 552 2000-10-21 ACIS-I 9.96 7.71 1.285 3.63 RXJ2129.6+0005ac 0.235 322.415 .088 9370 2009-04-03 ACIS-I 29.64 7.71 1.285 3.63 RXJ0439.0+0715b 0.244 69.752 7.266 1449 1999-10-16 ACIS-I 6.31 7.43 1.266 9.18 RXJ0439.0+0715b 0.244 69.752 7.266 3583 2003-01-04 ACIS-I 19.21 7.43 1.266 9.18 RXJ0439.0+0715b 0.244 69.752 7.266 526 1999-10-16 ACIS-I 1.59 7.43 1.266 9.18 Abell 521bc 0.248 73.530 10.223 430 2000-10-13 ACIS-S 39.11 11.43 1.459 4.78 Abell 521bc 0.248 73.530 −10.223 901 1999-12-23 ACIS-I 38.63 11.43 1.459 4.78 Abell 1835ac 0.253 210.258− 2.877 6880 2006-08-25 ACIS-I 117.91 12.29 1.493 2.04 Abell 1835ac 0.253 210.258 2.877 6881 2005-12-07 ACIS-I 36.28 12.29 1.493 2.04 Abell 1835ac 0.253 210.258 2.877 7370 2006-07-24 ACIS-I 39.50 12.29 1.493 2.04 RXJ0307.0-2840b 0.254 46.758 28.665 9414 2008-03-13 ACIS-I 18.90 7.83 1.282 1.27 − Note. — Right Ascension and Declination values are given in J2000 coordinates, in degrees, for the centroids of the clusters. Exposure times 14 are given in ks, and the values for M500 are given in units of 10 M . Table values for r500 are in Mpc, and absorption column densities are given 20 2 ⊙ in units of 10 cm− , as determined from the LAB survey of Galactic H I.

84 4.4. CANDIDATE SOURCE CATALOGS

Table 4.1. Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift.

Name z RA DEC OBS ID # Obs Date Detector Exposure (ks) M500 r500 NH Abell 68bc 0.255 9.274 9.160 3250 2002-09-07 ACIS-I 9.98 7.61 1.270 4.96 MS1455.0+2232 0.258 224.312 22.342 4192 2003-09-05 ACIS-I 91.88 6.23 1.187 3.18 MS1455.0+2232 0.258 224.312 22.342 543 2000-05-19 ACIS-I 9.85 6.23 1.187 3.18 MS1455.0+2232 0.258 224.312 22.342 7709 2007-03-23 ACIS-I 7.06 6.23 1.187 3.18 RXJ2011.3-5725b 0.279 302.863 57.419 4995 2004-06-08 ACIS-I 23.99 3.26 0.947 4.15 Abell 697bc 0.282 130.740− 36.365 4217 2002-12-15 ACIS-I 19.51 17.10 1.647 2.93 RXJ0232.2-4420b 0.284 38.073 44.348 4993 2004-06-08 ACIS-I 23.40 12.73 1.490 1.69 RXJ0528.9-3927b 0.284 82.221 −39.471 4994 2004-03-10 ACIS-I 22.45 13.31 1.515 2.10 ZW3146a 0.291 155.915− 4.186 909 2000-05-10 ACIS-I 46.00 9.43 1.348 2.46 ZW3146a 0.291 155.915 4.186 9371 2008-01-18 ACIS-I 40.16 9.43 1.348 2.46 RXJ0043.4-2037b 0.292 10.853 20.623 9409 2008-02-02 ACIS-I 19.91 8.08 1.277 1.84 1E0657-56b 0.296 104.614 −55.942 3184 2002-07-12 ACIS-I 87.47 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 4984 2004-08-19 ACIS-I 76.11 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 4985 2004-08-23 ACIS-I 27.48 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 4986 2004-08-25 ACIS-I 41.48 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 5356 2004-08-11 ACIS-I 97.19 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 5357 2004-08-14 ACIS-I 79.05 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 5358 2004-08-15 ACIS-I 31.95 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 5361 2004-08-17 ACIS-I 82.61 22.84 1.806 4.89 1E0657-56b 0.296 104.614 −55.942 554 2000-10-16 ACIS-I 25.79 22.84 1.806 4.89 Abell 2537bc 0.297 347.091 − 2.191 4962 2004-09-09 ACIS-S 36.19 7.15 1.225 4.62 Abell 2537bc 0.297 347.091 −2.191 9372 2008-08-11 ACIS-I 38.50 7.15 1.225 4.62 Abell781 0.298 140.105− 30.503 534 2000-10-03 ACIS-I 9.94 7.88 1.264 1.65 MACSJ2245.0+2637b 0.301 341.268 26.634 3287 2002-11-24 ACIS-I 16.86 4.94 1.080 5.04 MACSJ2311.5+0338 0.305 347.888 3.635 3288 2002-09-07 ACIS-I 13.59 17.35 1.635 4.60 MACSJ1131.8-1955b 0.306 172.981 19.929 3276 2002-06-14 ACIS-I 13.90 18.92 1.689 4.02 Abell 2744b 0.308 3.578 −30.388 2212 2001-09-03 ACIS-S 24.81 17.61 1.649 1.39 Abell 2744b 0.308 3.578 −30.388 7915 2006-11-08 ACIS-I 18.61 17.61 1.649 1.39 Abell 2744b 0.308 3.578 −30.388 8477 2007-06-10 ACIS-I 45.90 17.61 1.649 1.39 Abell 2744b 0.308 3.578 −30.388 8557 2007-06-14 ACIS-I 27.81 17.61 1.649 1.39 MS2137.3-2353ac 0.313 325.063 −23.661 4974 2003-11-13 ACIS-S 57.38 4.74 1.063 3.76 MS2137.3-2353ac 0.313 325.063 −23.661 5250 2003-11-18 ACIS-S 40.54 4.74 1.063 3.76 MS2137.3-2353ac 0.313 325.063 −23.661 928 1999-11-18 ACIS-S 43.60 4.74 1.063 3.76 MACSJ0242.5-2132a 0.314 40.649 −21.540 3266 2002-02-07 ACIS-I 11.85 7.73 1.249 2.72 Abell 1995b 0.316 223.241− 58.048 7021 2006-08-30 ACIS-I 48.53 5.95 1.145 1.19 Abell 1995b 0.316 223.241 58.048 906 2000-05-08 ACIS-S 45.56 5.95 1.145 1.19 MACSJ1427.6-2521a 0.318 216.914 25.350 3279 2002-06-29 ACIS-I 16.92 3.27 0.935 5.88 MACSJ1427.6-2521a 0.318 216.914 −25.350 9373 2008-06-11 ACIS-I 28.38 3.27 0.935 5.88 MACSJ0547.0-3904b 0.319 86.756 −39.073 3273 2002-10-20 ACIS-I 21.74 4.32 1.025 3.70 − Note. — Right Ascension and Declination values are given in J2000 coordinates, in degrees, for the centroids of the clusters. Exposure times 14 are given in ks, and the values for M500 are given in units of 10 M . Table values for r500 are in Mpc, and absorption column densities are given 20 2 ⊙ in units of 10 cm− , as determined from the LAB survey of Galactic H I.

WAVDETECT’s PSF information in determining the positions and size of candidate sources. The WAVDETECT software employs a single PSF model, and thus reliable PSF information cannot be supplied into WAVDETECT for the clusters with mixed ACIS-I and ACIS-S observations. Candi- date AGN for these clusters were therefore identiﬁed assuming the smallest possible wavelet scale

85 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Table 4.1. Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift.

Name z RA DEC OBS ID # Obs Date Detector Exposure (ks) M500 r500 NH MACSJ0257.6-2209b 0.322 44.421 22.153 3267 2001-11-12 ACIS-I 20.46 7.52 1.234 2.07 MACSJ2049.9-3217b 0.323 312.480 −32.280 3283 2002-12-08 ACIS-I 23.79 6.57 1.179 5.59 MACSJ2229.7-2755a 0.324 337.438 −27.926 3286 2002-11-13 ACIS-I 16.42 5.51 1.112 1.35 MACSJ2229.7-2755a 0.324 337.438 −27.926 9374 2007-12-09 ACIS-I 14.82 5.51 1.112 1.35 MACSJ1319.9+7003b 0.327 200.031− 70.077 3278 2002-09-15 ACIS-I 21.62 4.81 1.061 1.24 ZWCL1358+6245b 0.328 209.962 62.517 516 2000-09-03 ACIS-S 54.06 5.85 1.132 1.78 MACSJ0520.7-1328b 0.336 80.175 13.479 3272 2002-02-10 ACIS-I 19.23 6.34 1.160 7.29 CL0302-0423 0.350 45.587 − 4.389 5782 2005-12-07 ACIS-I 10.04 4.05 0.991 6.03 MACSJ1931.8-2634ac 0.352 292.956 −26.576 3282 2002-10-20 ACIS-I 13.59 9.94 1.339 8.31 MACSJ1931.8-2634ac 0.352 292.956 −26.576 9382 2008-08-21 ACIS-I 98.92 9.94 1.339 8.31 MACSJ0035.4-2015b 0.352 8.859 −20.262 3262 2003-01-22 ACIS-I 21.35 10.21 1.351 1.64 CL1212+2733 0.353 183.076− 27.550 5767 2005-03-17 ACIS-I 14.58 10.31 1.352 1.72 RBS797 0.354 146.804 76.387 2202 2000-10-20 ACIS-I 11.74 8.51 1.271 2.28 MACSJ1115.8+0129ac 0.355 168.966 1.498 3275 2003-01-23 ACIS-I 15.90 8.64 1.278 4.34 MACSJ1115.8+0129ac 0.355 168.966 1.498 9375 2008-02-03 ACIS-I 39.62 8.64 1.278 4.34 MACSJ0308.9+2645b 0.356 47.233 26.759 3268 2002-03-10 ACIS-I 24.44 16.39 1.580 9.43 MACSJ0404.6+1109b 0.358 61.136 11.136 3269 2002-02-20 ACIS-I 21.81 6.82 1.171 12.30 RXJ0027.6+2616 0.360 6.939 26.272 3249 2002-06-26 ACIS-I 9.97 5.03 1.061 3.58 RXJ1532.9+3021ac 0.363 233.224 30.349 1649 2001-08-26 ACIS-S 9.36 9.48 1.312 2.30 RXJ1532.9+3021ac 0.363 233.224 30.349 1665 2001-09-06 ACIS-I 9.97 9.48 1.312 2.30 CL0318-0302 0.370 49.638 3.049 5775 2005-03-15 ACIS-I 14.57 2.65 0.846 5.30 ZWCL1953bc 0.378 132.529− 36.072 1659 2000-10-22 ACIS-I 24.86 10.16 1.336 2.96 ZWCL1953bc 0.378 132.529 36.072 7716 2006-12-20 ACIS-I 6.98 10.16 1.336 2.96 MACSJ0011.7-1523a 0.379 2.928 15.389 3261 2002-11-20 ACIS-I 21.60 7.24 1.193 1.85 MACSJ0011.7-1523a 0.379 2.928 −15.389 6105 2005-06-28 ACIS-I 37.27 7.24 1.193 1.85 MACSJ0949.8+1708bc 0.384 147.465− 17.118 3274 2002-11-06 ACIS-I 14.31 11.35 1.380 3.08 MACSJ1720.2+3536ac 0.387 260.069 35.606 3280 2002-11-03 ACIS-I 20.84 6.31 1.135 3.46 MACSJ1720.2+3536ac 0.387 260.069 35.606 6107 2005-11-22 ACIS-I 33.88 6.31 1.135 3.46 MACSJ1731.6+2252bc 0.389 262.913 22.863 3281 2002-11-03 ACIS-I 20.50 12.82 1.427 4.99 MACSJ2211.7-0349bc 0.396 332.941 3.828 3284 2002-10-08 ACIS-I 17.73 18.06 1.608 5.53 MACSJ0429.6-0253ac 0.399 67.400 −2.884 3271 2002-02-07 ACIS-I 23.16 5.76 1.097 4.34 CL0809+2811 0.399 122.421− 28.200 5774 2004-11-30 ACIS-I 19.68 5.43 1.068 2.98 V1416+4446 0.400 214.116 44.778 541 1999-12-02 ACIS-I 31.15 2.54 0.834 0.76 MACSJ1006.9+3200 0.403 151.727 32.025 5819 2005-01-24 ACIS-I 10.88 11.07 1.360 1.52 MACSJ0159.8-0849 0.406 29.955 8.833 3265 2002-10-02 ACIS-I 17.90 10.77 1.348 2.06 MACSJ0159.8-0849 0.406 29.955 −8.833 6106 2004-12-04 ACIS-I 35.30 10.77 1.348 2.06 MACSJ2228.5+2036bc 0.411 337.136− 20.620 3285 2003-01-22 ACIS-I 19.85 14.67 1.491 4.26 MACSJ0152.5-2852b 0.413 28.141 28.892 3264 2002-09-17 ACIS-I 17.54 7.93 1.207 1.51 MACSJ0159.0-3412a 0.413 29.758 −34.218 5818 2006-02-19 ACIS-I 9.42 13.73 1.456 1.51 MACSJ1105.7-1014b 0.415 166.441 −10.243 5817 2005-01-03 ACIS-I 10.32 5.97 1.100 4.10 CL1003+3253 0.416 150.768− 32.893 5776 2005-03-11 ACIS-I 19.85 3.11 0.877 1.68 MACSJ2046.0-3430a 0.423 311.502 34.504 5816 2005-06-28 ACIS-I 10.03 4.16 0.975 4.59 MACSJ2046.0-3430a 0.423 311.502 −34.504 9377 2008-06-27 ACIS-I 39.23 4.16 0.975 4.59 MACSJ0451.9+0006bc 0.429 72.977 − .105 5815 2005-01-08 ACIS-I 10.21 6.33 1.118 6.85 Note. — Right Ascension and Declination values are given in J2000 coordinates, in degrees, for the centroids of the clusters. Exposure times are 14 given in ks, and the values for M500 are given in units of 10 M . Table values for r500 are in Mpc, and absorption column densities are given in 20 2 ⊙ units of 10 cm− , as determined from the LAB survey of Galactic H I.

86 4.4. CANDIDATE SOURCE CATALOGS

Table 4.1. Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift.

Name z RA DEC OBS ID # Obs Date Detector Exposure (ks) M500 r500 NH MACSJ0553.4-3342b 0.431 88.356 33.710 5813 2005-01-08 ACIS-I 9.94 15.05 1.492 3.32 MACSJ0358.8-2955b 0.434 59.722 −29.928 11719 2009-10-18 ACIS-I 9.64 15.84 1.500 0.98 MACSJ1226.8+2153 0.437 186.712− 21.831 12878 2011-04-11 ACIS-I 129.97 0.00 0.924 1.66 MACSJ1226.8+2153 0.437 186.712 21.831 3590 2003-12-13 ACIS-I 19.00 3.62 0.924 1.66 MACSJ1206.2-0847bc 0.439 181.551 8.801 3277 2002-12-15 ACIS-I 23.45 19.16 1.612 4.35 CL0141-3034 0.442 25.387 −30.578 5778 2005-06-04 ACIS-I 29.65 2.43 0.797 1.65 IRAS09104a 0.442 138.439− 40.940 509 1999-11-03 ACIS-S 9.05 8.30 1.217 1.42 MACSJ0417.5-1154bc 0.443 64.393 11.907 11759 2009-10-28 ACIS-I 51.35 22.06 1.689 3.31 MACSJ0417.5-1154bc 0.443 64.393 −11.907 12010 2009-10-29 ACIS-I 25.78 22.06 1.689 3.31 MACSJ0417.5-1154bc 0.443 64.393 −11.907 3270 2002-03-10 ACIS-I 12.01 22.06 1.689 3.31 MACSJ2243.3-0935bc 0.447 340.839 − 9.595 3260 2002-12-23 ACIS-I 20.50 17.35 1.555 4.02 MACSJ0455.2+0657b 0.447 73.821− 6.963 5812 2005-01-08 ACIS-I 9.94 9.10 1.244 8.41 MACSJ1359.1-1929a 0.447 209.792 19.489 5811 2005-03-17 ACIS-I 9.91 3.51 0.910 5.99 MACSJ0326.8-0043a 0.447 51.708 − .731 5810 2005-10-30 ACIS-I 9.91 4.70 1.004 6.76 MACSJ0329.6-0211ac 0.450 52.422 −2.195 3257 2001-11-25 ACIS-I 9.86 7.89 1.194 4.64 MACSJ0329.6-0211ac 0.450 52.422 −2.195 3582 2002-12-24 ACIS-I 19.84 7.89 1.194 4.64 MACSJ0329.6-0211ac 0.450 52.422 −2.195 6108 2004-12-06 ACIS-I 39.64 7.89 1.194 4.64 MACSJ0329.6-0211ac 0.450 52.422 −2.195 7719 2006-12-03 ACIS-I 7.08 7.89 1.194 4.64 RXJ1347.5-1145ac 0.451 206.878 −11.752 3592 2003-09-03 ACIS-I 57.71 21.71 1.674 4.60 RXJ1347.5-1145ac 0.451 206.878 −11.752 506 2000-03-05 ACIS-S 8.93 21.71 1.674 4.60 RXJ1347.5-1145ac 0.451 206.878 −11.752 507 2000-04-29 ACIS-S 9.99 21.71 1.674 4.60 MACSJ0140.0-0555b 0.451 25.004 − 5.918 5013 2004-06-04 ACIS-I 10.19 7.79 1.188 2.75 V1701+6414 0.453 255.346− 64.235 547 2000-10-31 ACIS-I 49.52 3.36 0.895 2.28 3C295a 0.460 212.834 52.202 2254 2001-05-18 ACIS-I 90.95 4.14 0.960 1.34 3C295a 0.460 212.834 52.202 578 1999-08-30 ACIS-S 18.79 4.14 0.960 1.34 MACSJ1621.3+3810ac 0.463 245.353 38.169 10785 2008-10-18 ACIS-I 29.75 5.89 1.078 1.13 MACSJ1621.3+3810ac 0.463 245.353 38.169 3254 2002-10-18 ACIS-I 9.84 5.89 1.078 1.13 MACSJ1621.3+3810ac 0.463 245.353 38.169 6109 2004-12-11 ACIS-I 37.54 5.89 1.078 1.13 MACSJ1621.3+3810ac 0.463 245.353 38.169 6172 2004-12-25 ACIS-I 29.75 5.89 1.078 1.13 MACSJ1621.3+3810ac 0.463 245.353 38.169 9379 2008-10-17 ACIS-I 29.91 5.89 1.078 1.13 CL1641+4001 0.464 250.472 40.029 3575 2003-09-24 ACIS-I 46.52 1.32 0.651 1.04 MACSJ1115.2+5320b 0.466 168.812 53.332 3253 2002-03-23 ACIS-I 8.77 12.73 1.391 0.89 MACSJ1115.2+5320b 0.466 168.812 53.332 5008 2004-06-22 ACIS-I 17.98 12.73 1.391 0.89 MACSJ1115.2+5320b 0.466 168.812 53.332 5350 2004-07-28 ACIS-I 6.87 12.73 1.391 0.89 MACSJ1108.8+0906bc 0.466 167.229 9.100 3252 2002-11-17 ACIS-I 9.94 7.73 1.179 2.22 MACSJ1108.8+0906bc 0.466 167.229 9.100 5009 2004-02-20 ACIS-I 24.46 7.73 1.179 2.22 CL0355-3741 0.473 58.997 37.695 5761 2006-01-12 ACIS-I 27.68 2.77 0.834 1.19 CL0333-2456 0.475 53.294 −24.942 5764 2005-04-05 ACIS-I 43.59 2.17 0.763 1.24 MACSJ0111.5+0855 0.485 17.880− 8.927 3256 2002-11-20 ACIS-I 19.38 2.40 0.790 4.52 MACSJ1427.2+4407ac 0.487 216.816 44.125 6112 2005-02-12 ACIS-I 9.38 6.35 1.095 1.19 MACSJ1427.2+4407ac 0.487 216.816 44.125 9380 2008-01-14 ACIS-I 25.81 6.35 1.095 1.19 MACSJ1427.2+4407ac 0.487 216.816 44.125 9808 2008-01-15 ACIS-I 14.93 6.35 1.095 1.19 MACSJ1311.0-0310a 0.494 197.757 3.177 3258 2002-12-15 ACIS-I 14.91 3.89 0.927 1.82 MACSJ1311.0-0310a 0.494 197.757 −3.177 6110 2005-04-20 ACIS-I 63.20 3.89 0.927 1.82 MACSJ1311.0-0310a 0.494 197.757 −3.177 7721 2007-03-03 ACIS-I 7.05 3.89 0.927 1.82 MACSJ1311.0-0310a 0.494 197.757 −3.177 9381 2007-12-09 ACIS-I 29.73 3.89 0.927 1.82 − Note. — Right Ascension and Declination values are given in J2000 coordinates, in degrees, for the centroids of the clusters. Exposure times 14 are given in ks, and the values for M500 are given in units of 10 M . Table values for r500 are in Mpc, and absorption column densities are given 20 2 ⊙ in units of 10 cm− , as determined from the LAB survey of Galactic H I.

87 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Table 4.1. Continued-Summary of the cluster sample and Chandra observations listed in order of cluster redshift.

Name z RA DEC OBS ID # Obs Date Detector Exposure (ks) M500 r500 NH CL1002+6858 0.500 150.537 68.976 5773 2005-01-05 ACIS-I 19.79 4.04 0.935 5.19 RXJ003033.2+261819 0.500 7.641 26.302 5762 2005-05-28 ACIS-I 17.88 2.71 0.819 3.71 MACSJ2214.9-1359bc 0.502 333.738 14.003 3259 2002-12-22 ACIS-I 19.47 13.16 1.387 2.88 MACSJ2214.9-1359bc 0.502 333.738 −14.003 5011 2003-11-17 ACIS-I 18.52 13.16 1.387 2.88 MACSJ0911.2+1746bc 0.505 137.795− 17.775 3587 2003-02-23 ACIS-I 17.87 8.96 1.220 3.28 MACSJ0911.2+1746bc 0.505 137.795 17.775 5012 2004-03-08 ACIS-I 23.79 8.96 1.220 3.28 MACSJ0257.1-2325bc 0.505 44.287 23.434 1654 2000-10-03 ACIS-I 19.84 8.51 1.198 2.08 MACSJ0257.1-2325bc 0.505 44.287 −23.434 3581 2003-08-23 ACIS-I 18.47 8.51 1.198 2.08 V1525+0958 0.516 231.165− 9.962 1664 2002-04-01 ACIS-I 50.87 4.24 0.944 2.72 CL1357+6232 0.525 209.323 62.547 5763 2006-01-24 ACIS-I 25.68 2.68 0.808 1.83 CL1357+6232 0.525 209.323 62.547 7267 2006-01-29 ACIS-I 18.21 2.68 0.808 1.83 MACSJ0454.1-0300c 0.538 73.547 3.014 529 2000-01-14 ACIS-I 13.90 11.46 1.308 3.92 MACSJ0454.1-0300c 0.538 73.547 −3.014 902 2000-10-08 ACIS-S 44.19 11.46 1.308 3.92 MACSJ1423.8+2404ac 0.543 215.949− 24.078 1657 2001-06-01 ACIS-I 18.52 6.65 1.088 2.20 MACSJ1423.8+2404ac 0.543 215.949 24.078 4195 2003-08-18 ACIS-S 115.57 6.65 1.088 2.20 MACSJ1149.5+2223bc 0.544 177.397 22.401 1656 2001-06-01 ACIS-I 18.52 18.66 1.533 1.92 MACSJ1149.5+2223bc 0.544 177.397 22.401 3589 2003-02-07 ACIS-I 20.04 18.66 1.533 1.92 MACSJ0717.5+3745bc 0.546 109.383 37.755 1655 2001-01-29 ACIS-I 19.87 24.87 1.688 6.64 MACSJ0717.5+3745bc 0.546 109.383 37.755 4200 2003-01-08 ACIS-I 59.16 24.87 1.688 6.64 MS0015.9+1609bc 0.547 4.639 16.436 520 2000-08-18 ACIS-I 67.41 16.47 1.469 3.99 V1121+2327 0.562 170.239 23.441 1660 2001-04-23 ACIS-I 71.24 3.42 0.845 1.14 CL0216-1747 0.578 34.138 17.792 5760 2005-09-07 ACIS-I 40.04 1.55 0.658 3.02 CL0216-1747 0.578 34.138 −17.792 6393 2005-10-04 ACIS-I 26.64 1.55 0.658 3.02 MACSJ0025.4-1222bc 0.585 6.374 −12.379 10413 2008-10-16 ACIS-I 75.63 7.59 1.119 2.50 MACSJ0025.4-1222bc 0.585 6.374 −12.379 10786 2008-10-18 ACIS-I 14.12 7.59 1.119 2.50 MACSJ0025.4-1222bc 0.585 6.374 −12.379 10797 2008-10-21 ACIS-I 23.85 7.59 1.119 2.50 MACSJ0025.4-1222bc 0.585 6.374 −12.379 3251 2002-11-11 ACIS-I 19.32 7.59 1.119 2.50 MACSJ0025.4-1222bc 0.585 6.374 −12.379 5010 2004-08-09 ACIS-I 24.82 7.59 1.119 2.50 CL0956+4107 0.587 149.013− 41.118 5294 2003-12-30 ACIS-I 17.34 2.65 0.784 1.22 CL0956+4107 0.587 149.013 41.118 5759 2005-01-28 ACIS-I 40.16 2.65 0.784 1.22 MACSJ2129.4-0741bc 0.588 322.357 7.691 3199 2002-12-23 ACIS-I 19.85 10.65 1.250 4.33 MACSJ2129.4-0741bc 0.588 322.357 −7.691 3595 2003-10-18 ACIS-I 19.87 10.65 1.250 4.33 CL0328-2140 0.590 52.150 −21.672 5755 2005-03-15 ACIS-I 43.29 3.51 0.859 2.11 CL0328-2140 0.590 52.150 −21.672 6258 2005-03-18 ACIS-I 13.09 3.51 0.859 2.11 MACSJ0647.7+7015bc 0.592 101.957− 70.248 3196 2002-10-31 ACIS-I 19.27 10.88 1.257 5.40 MACSJ0647.7+7015bc 0.592 101.957 70.248 3584 2003-10-07 ACIS-I 19.99 10.88 1.257 5.40 CL1120+4318 0.600 170.028 43.301 5771 2005-01-11 ACIS-I 19.83 5.31 0.984 2.97 CL1334+5031 0.620 203.583 50.516 5772 2005-08-05 ACIS-I 19.49 3.63 0.854 1.05 CLJ0542.8-4100 0.642 85.708 41.000 914 2000-07-26 ACIS-I 50.40 5.63 0.985 3.18 CL1202+5751 0.677 180.574− 57.864 5757 2005-09-02 ACIS-I 58.98 3.18 0.804 1.74 CL0405-4100 0.686 61.352 41.005 5756 2005-10-27 ACIS-I 7.94 2.28 0.718 1.27 CL0405-4100 0.686 61.352 −41.005 7191 2006-05-19 ACIS-I 69.21 2.28 0.718 1.27 MACSJ0744.8+3927bc 0.698 116.217− 39.457 3197 2001-11-12 ACIS-I 20.23 12.53 1.264 5.66 MACSJ0744.8+3927bc 0.698 116.217 39.457 3585 2003-01-04 ACIS-I 19.85 12.53 1.264 5.66 MACSJ0744.8+3927bc 0.698 116.217 39.457 6111 2004-12-03 ACIS-I 49.50 12.53 1.264 5.66 V1221+4918 0.700 185.358 49.308 1662 2001-08-05 ACIS-I 79.08 5.72 0.971 1.54 CLJ0152.7-1357 0.833 28.171 13.968 913 2000-09-08 ACIS-I 36.48 7.78 0.972 1.33 CLJ1226.9+3332b 0.888 186.741− 33.546 3180 2003-01-27 ACIS-I 31.69 7.78 0.999 1.83 CLJ1226.9+3332b 0.888 186.741 33.546 5014 2004-08-07 ACIS-I 32.71 7.78 0.999 1.83 CLJ1226.9+3332b 0.888 186.741 33.546 932 2000-07-31 ACIS-S 9.82 7.78 0.999 1.83 Note. — Right Ascension and Declination values are given in J2000 coordinates, in degrees, for the centroids of the clusters. Exposure times 14 are given in ks, and the values for M500 are given in units of 10 M . Table values for r500 are in Mpc, and absorption column densities are given 20 2 ⊙ in units of 10 cm− , as determined from the LAB survey of Galactic H I.

88 4.5. FOLLOW-UP ANALYSIS

(1 pixel), resulting in a boosted number of candidate sources. However, our subsequent screening processes are efficient at removing spurious sources, leading to the results for these clusters being consistent with the rest. Tests show that it is unnecessary to subsequently run WAVDETECT on the hard-band and soft-band images separately, as only a few additional sources per cluster field are identified in these images. Moreover, the majority of these additional sources detected in the hard or soft bands are subsequently rejected after further analysis.

4.5 Follow-Up Analysis

Given our liberal false-positive probability threshold, incomplete incorporation of the Chandra PSF, and the bright background ICM present at the center of every field of view, we expect that the initial catalogs produced by WAVDETECT have an appreciable number of spurious sources. Further analysis is required to ensure source validity. To achieve this, every candidate source in the initial catalog is re-analyzed on an observation-by-observation basis using the ACIS-EXTRACT point- source analysis software package 2 (Broos et al., 2010). This offers several key improvements, specifically: (1) utilization of source and background regions that approximate the shape of the PSF calculated from ray-tracing simulations, accommodating neighboring sources and CCD chip boundaries as needed; (2) a multi-stage approach to source detection that refines the source catalog based on each source’s binomial no-source probability (i.e. the probability of a source not being real given local backgrounds); and (3) better source-position determinations that improve the accuracy of each source’s astrometry and photometry. We refer the reader to Broos et al. (2010) for documentation about ACIS-EXTRACT, although the most relevant aspects of the analysis are discussed below. Our analysis pipeline has three main stages and follows the recent study of the 4-Ms CDFS (Xue et al., 2011), modified to accommodate the higher background rates and shorter exposure times of our cluster observations. Unless otherwise noted, all calculations were performed in the full band (0.5 8.0 keV). −

Removing False Sources

For each observation of every candidate point source, ACIS-EXTRACT is used to compute a polygonal source-extraction region based on models of the Chandra PSF at the source position. These

2The ACIS Extract software package and User’s Guide are available at http://www.astro.psu.edu/xray/acis/acis analysis.html.

89 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS Number

0.1−20 1 −15 10 100 −10 1000 −5 0

log(Pb)

Figure 4.1: The distribution of X-ray no-source binomial probabilities, Pb, for all sources before inclusion in the final catalog. For this figure, probabilities were determined from counts in the full band (0.5 8.0 keV). The minimum threshold chosen for inclusion in the final 3 − catalog, Pb < 10− , is denoted by the dashed vertical line. For presentation purposes, we set log Pb = 20 for all sources where log Pb 20. − ≤ − models use simulations of the Chandra High Resolution Mirror Assembly using the MARX ray- tracing simulator. Polygonal extraction regions were constructed that approximate the 90% en- ∼ circled energy fraction (EEF) contour of the local PSF at 1.497 keV. When dealing with crowded sources that have overlapping regions, ACIS-EXTRACT utilizes smaller extraction regions (corresponding to 40 75% EEFs) chosen to be as large as possible without overlapping. These ∼ − source regions were used throughout the analysis, including the determination of the source’s local background. For our analysis, the ACIS-EXTRACT “BETTER BACKGROUNDS” algorithm was utilized for background extraction, which computes the background counts within external background regions surrounding the source, accounting for contributions from the source and neighboring sources. These background regions were required to contain a minimum of 100 counts, and had a median of 111 counts. In order to provide the most accurate possible backgrounds, front-illuminated and back-illuminated chips in a particular observation were treated as separate observations. In order to refine the catalog and remove spurious sources, we utilize the no-source binomial

90 4.5. FOLLOW-UP ANALYSIS

probability to determine the likelihood that source counts are due to ﬂuctuations in the background (see Appendix A of Weisskopf et al., 2007). If there are a total of S counts in a source region

(subtending a solid angle of Ωsrc) and Bext counts in the external background region (subtending a solid angle of Ωext), the binomial probability can be calculated as:

N! X N X Pb = p (1 p) − (4.1) (N X)!X! − X S ≥ −

In this equation, N is the total number of counts in the source+background region (S +Bext), and p= 1/(1 + BACKSCAL) is the probability that a background count is located within the source region (thus contributing to S ), where BACKSCAL=Ωext/Ωsrc. Since ACIS-EXTRACT calculates the expected number of background counts in the source region (Bsrc) from this same area ratio, BACKSCAL is also equivalent to the ratio of background counts in the external background region and source regions (Bext/Bsrc). The median value of BACKSCAL for all sources included in the final catalog is roughly 62. In the first stage of the pipeline, this probability was determined for each source and all sources with Pb 0.01 (i.e. a probability of being a background fluctuation of more ≥ than one percent) were removed. This ensures that the source and background regions for “real” sources are not influenced by the presence of spurious sources nearby. Although the results vary from cluster to cluster, roughly 20 25% of the initial WAVDETECT sources were removed by ∼ − this step. Counterpart matching rates between these rejected sources and optical source catalogs are consistent with those expected solely from random coincidence, which suggests that the majority of these sources are indeed spurious and that few valid AGN are removed by this procedure. This evidence is strengthened by visual inspection of the rejected sources, which independently suggests that the vast majority of the rejected sources are indeed spurious.

Improving Source Positions

After removal of the most likely spurious candidate sources, source and background regions were redetermined for all surviving sources. These source regions were analyzed carefully to estimate the best centroid positions. The source positions determined by WAVDETECT do not utilize the full information about the local PSF, in particular at large off-axis angles where the shape of the PSF becomes complex. This is especially true for sources with “mixed” observations (using both ACIS-I and ACIS-S), where no PSF information could be input to WAVDETECT in our analysis. We used ACIS-EXTRACT to improve the source positions, utilizing three different algorithms: (1) calculating the centroid from the data (DATA); (2) correlating the PSF within the neighboring

91 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

region (CORR); and (3) reconstructing an image of the region neighboring the source using the background and source information and a maximum likelihood algorithm (ML). Tests carried out by the ACIS-EXTRACT team (Broos et al., 2010) provide guidance as to which of these algorithms is best suited for a particular source, based on its position and the positions of neighboring sources. Following their recommendations, we have moved all sources to their DATA positions when they were uncrowded and located near the aimpoint (within 8′); to their CORR positions when the sources were far from the aimpoint; and to their ML positions whenever other sources are sufﬁciently close that the wings of their respective PSF’s may overlap. The typical displacement from the original WAVDETECT positions is . 1 arcsec.

Source Photometry and Spectroscopy

With the source positions set, final source products were extracted from each observation. For each new source position, the PSF was simulated by ACIS-EXTRACT at the nominal PSF energy (1.497 keV) as well as four other energies (0.277, 4.510, 6.400, and 8.600 in order to determine the best estimate of the PSF. Background regions and models were recalculated for every source, and spectral products including ancillary response files and response matrices were extracted, based on the model PSF region. The spectral products, PSF models, and no-source probabilities were combined for sources observed multiple times. In order to determine source fluxes, we utilized the photon flux measurements for each source computed by ACIS-EXTRACT3 and converted them to energy fluxes by assuming a power-law model with photon index Γ = 1.4. This value is consistent with the expected photon index for the field population of AGN. Spectral analyses of the individual sources in our catalog are consistent with this assumption. Statistical uncertainties on the source fluxes are typically at the 30% level, taking into account uncertainties in the source count rates, ∼ background, and best-fit power-law model parameters. For sources with successful spectral fits, the median photon index is Γ= 1.43, while the mean is Γ= 1.37. We have also extracted the emission spectrum for each source and fit each sources’ spectra with an absorbed power-law model. Such a good model is a good first-order approximation for the X- ray spectra of these sources, especially given the small net counts we have for the majority of our

3We have used both the “FLUX1” and “FLUX2” measurements made by ACIS-EXTRACT. The “FLUX1” measurement is deﬁned as the net counts in each channel divided by the ACIS ancillary response in the channel (for that source position in the same energy band) and the exposure time, summed over all channels. The “FLUX2” measurement is deﬁned as the net counts in an energy band divided by the mean ancillary response of the ACIS detector and the exposure time. Both of these measurements convert the 2 1 measured counts into units of photons cm− s− .

92 4.5. FOLLOW-UP ANALYSIS

sources.

Final Source Selection

3 Our threshold for inclusion in the final point source catalog is Pb < 10− in any band. These thresholds were chosen to be conservative. As a comparison, the CDFS uses a threshold of Pb < 0.004 in any band and led to a sample of high purity (i.e. a catalog with few spurious detections expected based on simulations and optical counterpart matching rates). The distribution of Pb in the full band for all sources is shown in Figure 4.1. Using estimates of the PSF and local background, we are able to efficiently remove the majority of spurious sources identified by WAVDETECT, including sources associated with diffuse, extended ICM emission. However, we also investigated the central regions of every galaxy cluster visually. Despite the best attempts of ACIS-EXTRACT to suppress sources associated with the diffuse ICM, structures that vary significantly on angular scales comparable to the Chandra PSF can be mistaken for point sources at high formal significance, and can only be reliably removed with visual inspection. The vast majority of these sources are located within 100 kpc of the cluster centroids, and ∼ are predominantly located in clusters identified as hosting cool cores (e.g. Dunn & Fabian, 2008; Hlavacek-Larrondo et al., 2012). Cool core clusters typically have sharply peaked surface brightness profiles (e.g. Peterson & Fabian, 2006). The presence of a cool core is also closely linked to AGN feedback by the cluster’s central galaxy (e.g. Fabian et al., 2003, 2006; McNamara & Nulsen, 2007; Sanders & Fabian, 2007; Million et al., 2010a; Werner et al., 2010), and ICM substructures arising from this feedback such as shock fronts or cavities can also result in structures falsely classified as bright point sources. Since the cool cores are typically associated with the centers of clusters where the galaxy density is also highest, these sources also commonly have optical counterparts by random coincidence. Examples of such false candidate sources are shown in Figure 4.2 in the extreme cool core cluster MACS J1931.8-2634 (Ehlert et al., 2011). In our visual inspections, we were conservative, removing all candidate sources that could not be clearly identified as bright AGN visually. It is possible, therefore, that our results slightly underestimate the AGN density in the innermost regions of the clusters (r . 50 kpc). Since the cluster emission is brightest and most structured near the cluster center, the properties of all point sources identified in these regions will be subject to systematic uncertainties due to potential errors in the background estimation. Additionally, these visual inspections enabled us to manually remove sources that were correlated with instrumental artifacts unrelated to physical point sources.

93 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Source Extension

We have performed a check for spatial extension of our sources using the Kolmogorov-Smirnov (K-S) test discussed by Xue et al. (2011). For each detected X-ray source we derived cummulative EEFs for both the PSF model and source counts out to 90% EEF. We then used a K-S test to compute the probability (PKS) that the two sets of cummulative EEFs are consistent with one another.

Approximately 6% have a measured value of PKS < 0.05 (i.e. have a distribution of source counts inconsistent with the simulated PSF at 95% conﬁdence), and approximately 3% have PKS < 0.01. ≥ These results are consistent with measurements from the CDFS (Xue et al., 2011). We conclude that at most 2% of the sources may have spatial extension. Visual inspection of the sources with ∼ PKS < 0.01, however, suggests that the majority of these sources do not have signiﬁcant extension beyond the PSF, especially for sources observed multiple times at different off-axis angles. We therefore treat these results as a conservative upper limit to the true number of extended sources in our catalogs.

4.6 Background and Sensitivity Calculation

The minimum flux at which point sources can be reliably detected in a given Chandra observation and energy band depends on the exposure time of the observation, the local background, vignetting corrections, the local PSF, the assumed properties of the source, and the manner in which point sources are selected. For each cluster field, we calculate the flux limit at every position in the field of view for a canonical AGN source, using the effective exposure maps, background maps, and the no-source binomial probability (Equation 4.1). Sensitivity maps were created in all three science bands.

4.6.1 Background Map Creation

Background maps for each cluster and energy band were created directly from the cluster data, masking all point sources in the final catalogs. Masked regions were re-filled using the CIAO routine dmfilth, which replaces the excised regions with random values drawn from a Poisson distribution, the mean of which is sampled from a unique background region for each source. For the excised source regions, we used circles with radii 1.5 times the 99% enclosed energy fraction radii for each source. For refilling, we used annuli with inner and outer radii of 1.5 and 2.5 times the ∼ 94 4.6. BACKGROUND AND SENSITIVITY CALCULATION

99% enclosed energy fraction radii. Approximately 25% of the pixels in each image were re- ∼ ﬁlled by this procedure. The resulting background map is the sum of unresolved cosmic sources, diffuse cluster emission, Galactic foreground emission, and instrumental background from each observation. In this study, resolving the relative contributions of these background components is not important, as the sensitivity to point sources only depends on the total background present in each pixel.

4.6.2 Deriving the Sensitivity Maps

With the total background maps in hand, we determined the ﬂux limit for point sources anywhere in a given cluster ﬁeld. We determine the minimum number of counts required for source detection by 3 solving Equation 4.1 for our catalog threshold no-source binomial probability, Pb < 10− . To do so, we require estimates of the background counts in both a source aperture (Bsrc) and an external background region (Bext), both of which will depend strongly on the local PSF. For these calculations, the PSF was assumed to be a circular aperture with a radius that depends only on the angular distance between the source position and the exposure-weighted aimpoint, αp. The radius of the aperture was estimated using the 90% EEF radius of point sources at the same off-axis angle. Determinations ∼ of the background counts in the source aperture, Bsrc, are taken directly from the background maps discussed above, using the local PSF aperture. Estimates of the external background counts, Bext, are determined from the Bext values used in the cluster point source catalog. We set the Bext to the maximum Bext value of the cluster-catalog sources that are located in the same annular region as the position in question, with the inner/outer radii being αp 1.0 and αp + 1.0 . By choosing the − ′ ′ maximum value of Bext, the ratio BACKSCAL is maximized for every source aperture, meaning that the counts required for source detection in an aperture is minimized.

With estimates of Bsrc and Bext for each position in the background map, we numerically solve the binomial probability equation to determine the minimum number of counts required for detection under the source-detection criterion. These counts are then converted into a count rate given the effective exposure map, and the count rate is converted into a physical ﬂux assuming an absorbed power-law spectrum with a photon index of Γ = 1.4. The above procedure takes into account PSF broadening with off-axis angle, the variations in the effective exposure time (due to vignetting and CCD chip gaps, for example), and variations in the background across the ﬁeld of view, including

95 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

the diffuse galaxy cluster emission. 4 An example sensitivity map, for the cluster MACS J1931.8- 2634, is shown in Figure 4.3. The impact of the diffuse cluster emission on the local flux limit is apparent at the center of the sensitivity map, where the local flux limit is a factor of 5 10 times ∼ − higher at the cluster center compared to the surrounding region, despite the significantly sharper PSF at the cluster center. We define the band-specific flux limit for each cluster field as the minimum flux in that energy band to which 50% of that particular field is sensitive. These flux limits range from 1 9 10 15 erg cm 2 s 1 in the full band, and are shown for each cluster and energy − × − − − band in Table 4.2.

4.7 The Final X-ray AGN Survey

The final point-source catalog has a total of 11671 X-ray bright point sources detected at our selec- 3 2 tion criteria (Pb < 10 in any band) across a total survey area of 12.1 deg . In the full (0.5 8.0 keV), − − soft (0.5 2.0 keV), and hard (2.0 8.0 keV) bands, a total of 11328, 9244, and 7128 sources, respec- − − tively, satisfy the selection criteria for that specific band. The majority of these sources are detected in at least two bands. Only 747, 247, and 96 sources satisfy the selection criteria exclusively in the full band, soft band, and hard band, respectively. Treating the no-source binomial probability formally, there is a . 5% chance of there being more than 15 spurious sources in any band. 5 General information about the point source catalogs for each cluster can be found in Table 4.2. As an example, the initial and final point source positions for the galaxy cluster MACS J1931.8-2634 are shown in Figure 4.4.

4Only one sensitivity map is determined for each cluster, as this procedure is equally valid for clusters observed multiple times. The only difference is that the background maps and exposure maps from each individual exposure are summed together before the local flux limit is calculated. The median distance between any observation aimpoint and the mean aimpoint is 0.7′, therefore changes in the local PSF between observations should not be significant. For a few clusters,∼ observations have aimpoints appreciably far away from the mean aimpoint (& 2′), which adds a source of systematic uncertainty to the sensitivity maps. These systematic uncertainties are not large compared to the total survey area over all fields, and are therefore inconsequential for the final results. 5Since the no-source binomial probability is a formally derived null-hypothesis test (Weisskopf et al., 2007), the true false-positive rate should not deviate significantly from these estimates. This is slightly complicated by the possibility of extended sources at high significance, but nevertheless we expect that these catalogs are not contaminated by large numbers of spurious sources.

96 4.7. THE FINAL X-RAY AGN SURVEY

Table 4.2: Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize × − − − a mix of ACIS-S and ACIS-I observations.

Cluster Name Nsoft/Nhard/Nfull/Nany Flux Limit ABELL2163 68/57/83/90 1.12/3.55/2.82 Abell 520 95/78/121/133 1.26/5.62/3.98 Abell 209 36/33/50/56 2.00/6.31/5.01 Abell 963a 61/45/75/79 2.51/14.13/8.91 RXJ0439.0+0520 76/72/103/107 1.26/5.01/3.98 Abell 1423 27/18/33/39 2.82/10.00/7.08 ZWICKY2701 49/29/58/62 1.26/5.01/3.55 RXJ1504.1-0248 73/54/88/93 1.12/4.47/3.16 Abell 773 72/72/96/103 1.78/7.08/5.01 RXJ0304.1-3656 40/30/55/59 1.78/7.08/5.01 RXJ0237.4-2630 43/31/53/54 2.00/7.94/5.62 Abell 2261 59/48/68/75 1.58/5.62/3.98 Abell 1682 33/24/40/51 3.55/12.59/10.00 Abell 2667 23/19/29/34 3.16/11.22/7.94 RXJ0638.7-5358 44/36/54/60 2.00/7.08/5.01 Abell 1763 47/38/59/64 2.00/6.31/5.01 RXJ0220.9-3829 52/41/63/65 1.78/7.08/5.01 Abell 2219 31/18/34/40 1.26/4.47/3.16 Abell 2111 38/19/46/50 3.16/10.00/7.94 Z5247 30/21/36/36 3.55/11.22/7.94 Abell 2390 69/54/88/93 1.58/10.00/5.62 Z2089 29/19/37/37 2.82/11.22/7.94 RXJ2129.6+0005 81/54/99/109 1.78/7.08/5.01

97 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Figure 4.2: An example of spurious candidate sources initially identified within the central 1 of MACS J1931.8-2634. Contours show the respective PSF region ( 90% EEF) for ∼ ′ ∼ each candidate source. This particular cluster shows evidence for both a recent merger and powerful AGN feedback (Ehlert et al., 2011), which results in the sharp fronts and cavities in the 50 kpc surrounding the cluster center. The black source has been identified as spurious ∼ 3 automatically by ACIS-EXTRACT (Pb 10 ), while the white sources had to be removed ≥ − manually. Although these sources are significant compared to their local background, they are not AGN. Such candidate sources can only be reliably removed by visual inspection. The only true AGN source included in the final catalog is the magenta source at the center. This is one 43 1 of only a few luminous cD galaxies in this sample (LX 8 10 erg s ). ∼ × −

98 4.7. THE FINAL X-RAY AGN SURVEY

Table 4.2: Continued

Cluster Name Nsoft/Nhard/Nfull/Nany Flux Limit RXJ0439.0+0715 59/42/69/86 2.51/10.00/7.08 Abell 521a 110/81/133/141 1.00/4.47/3.16 Abell 1835 137/115/171/177 0.79/3.16/2.51 RXJ0307.0-2840 55/29/61/65 2.00/7.08/5.01 Abell 68 36/30/47/50 2.82/10.00/7.08 MS1455.0+2232 130/96/142/158 0.89/3.55/2.51 RXJ2011.3-5725 65/57/79/82 1.41/5.62/3.98 Abell 697 51/42/66/69 1.78/6.31/5.01 RXJ0232.2-4420 42/35/55/58 1.78/7.08/5.01 RXJ0528.9-3927 64/37/67/72 1.78/7.08/5.01 ZW3146 111/78/128/140 1.12/4.47/3.16 RXJ0043.4-2037 46/29/54/58 1.78/7.08/5.01 1E0657-56 267/196/309/340 0.50/2.24/1.41 Abell 2537a 80/63/100/104 1.12/5.01/3.55 Abell 781 34/21/37/41 3.16/8.91/7.08 MACSJ2245.0+2637 44/39/59/62 1.78/7.08/5.01 MACSJ2311.5+0338 37/21/46/50 2.51/10.00/7.08 MACSJ1131.8-1955 46/28/58/59 2.24/7.94/6.31 Abell 2744a 120/98/150/159 1.26/5.01/3.55 MS2137.3-2353 54/44/66/68 0.56/2.82/1.78 MACSJ0242.5-2132 28/20/36/37 2.51/8.91/6.31 Abell 1995 118/83/138/148 0.89/3.55/2.51 MACSJ1427.6-2521 75/65/91/93 1.00/10.00/7.08 MACSJ0547.0-3904 61/44/73/77 1.41/5.62/3.98 MACSJ0257.6-2209 55/41/70/72 1.58/5.62/3.98

99 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Table 4.2: Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize × − − − a mix of ACIS-S and ACIS-I observations.

Cluster Name Nsoft/Nhard/Nfull/Nany Flux Limit MACSJ2049.9-3217 49/43/67/73 1.41/5.01/3.98 MACSJ2229.7-2755 55/37/62/66 1.26/5.01/3.55 MACSJ1319.9+7003 58/46/74/78 1.78/7.08/4.47 ZWCL1358+6245 67/40/73/84 0.79/3.55/2.24 MACSJ0520.7-1328 49/32/60/65 1.58/6.31/4.47 CL0302-0423 28/25/38/42 2.51/11.22/7.08 MACSJ1931.8-2634 104/92/133/137 0.63/2.51/1.78 MACSJ0035.4-2015 46/40/59/61 1.58/5.62/3.98 CL1212+2733 40/34/54/56 2.24/7.94/5.62 RBS797 46/26/52/54 2.24/7.94/5.62 MACSJ1115.8+0129 76/63/92/94 0.89/3.55/2.51 MACSJ0308.9+2645 38/30/46/52 1.58/5.62/3.98 MACSJ0404.6+1109 44/36/56/57 1.41/5.62/3.98 RXJ0027.6+2616 23/19/32/36 2.51/10.00/7.08 RXJ1532.9+3021a 37/24/41/46 2.00/7.08/6.31 CL0318-0302 43/31/51/54 2.00/7.94/5.62 ZWCL1953 83/51/101/106 1.41/5.62/3.98 MACSJ0011.7-1523 106/93/135/144 1.41/5.62/3.98 MACSJ0949.8+1708 39/34/48/49 2.24/7.08/5.62 MACSJ1720.2+3536 100/81/119/125 0.89/3.55/2.82 MACSJ1731.6+2252 64/48/79/83 1.58/5.62/4.47 MACSJ2211.7-0349 53/43/73/80 1.78/7.08/5.01 MACSJ0429.6-0253 68/58/87/91 1.41/5.62/3.98 CL0809+2811 36/26/47/52 1.78/7.08/5.01 V1416+4446 68/48/83/94 1.12/4.47/3.16 MACSJ1006.9+3200 45/31/54/55 2.51/10.00/6.31 MACSJ0159.8-0849 64/50/81/88 1.12/5.62/3.16 MACSJ2228.5+2036 60/51/75/78 1.58/6.31/4.47 MACSJ0152.5-2852 37/32/47/48 1.78/6.31/4.47

100 4.7. THE FINAL X-RAY AGN SURVEY

Table 4.2: Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize × − − − a mix of ACIS-S and ACIS-I observations.

Cluster Name Nsoft/Nhard/Nfull/Nany Flux Limit MACSJ0159.0-3412 39/32/45/47 2.82/11.22/7.94 MACSJ1105.7-1014 35/25/42/44 2.51/10.00/7.08 CL1003+3253 56/39/66/69 1.78/6.31/4.47 MACSJ2046.0-3430 72/64/89/91 0.89/3.55/2.51 MACSJ0451.9+0006 39/31/50/52 2.82/10.00/7.08 MACSJ0553.4-3342 23/18/35/37 2.82/10.00/7.08 MACSJ0358.8-2955 24/23/36/37 3.16/11.22/7.94 MACSJ1226.8+2153 119/111/154/76 1.78/6.31/4.47 MACSJ1206.2-0847 59/53/84/86 1.41/5.01/3.98 CL0141-3034 55/44/70/74 1.26/5.01/3.55 IRAS09104 22/18/29/32 2.51/11.22/7.08 MACSJ0417.5-1154 100/87/125/131 1.00/4.47/4.47 MACSJ2243.3-0935 48/38/58/62 1.78/5.62/4.47 MACSJ0455.2+0657 41/23/48/49 2.51/10.00/7.08 MACSJ1359.1-1929 34/23/43/44 2.51/10.00/7.08 MACSJ0326.8-0043 25/15/34/36 2.51/10.00/7.08 MACSJ0329.6-0211 74/62/94/102 0.89/3.55/2.51 RXJ1347.5-1145 57/56/79/86 1.00/5.01/2.82 MACSJ0140.0-0555 31/18/36/36 2.51/10.00/7.08 V1701+6414 63/51/80/84 1.00/3.55/2.51 3C295 122/101/145/153 0.56/2.51/1.58 MACSJ1621.3+3810 134/112/154/159 0.71/2.82/2.00 CL1641+4001 83/74/97/100 0.89/3.55/2.51 MACSJ1115.2+5320 78/61/96/104 2.00/7.94/5.62 MACSJ1108.8+0906 57/46/76/80 1.26/5.01/3.55 CL0355-3741 42/34/50/56 1.41/5.62/3.55 CL0333-2456 68/57/89/93 1.00/4.47/3.16 MACSJ0111.5+0855 66/52/78/79 1.58/5.62/3.98

101 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Table 4.2: Summary of X-ray point source numbers and flux limits for each cluster field. The 3 columns list (1) cluster name; (2) the number of AGN satisfying Pb < 10− in the soft band, the hard band, the full band, and in any band, respectively; (3) the flux limit for each cluster observation in the soft, hard, and full bands, defined as the minimum flux to which 50% of the survey area is sensitive, in units of 10 15 erg cm 2 s 1 . Observations denoted with an a utilize × − − − a mix of ACIS-S and ACIS-I observations.

Cluster Name Nsoft/Nhard/Nfull/Nany Flux Limit MACSJ1427.2+4407 72/61/93/95 1.00/3.98/2.82 MACSJ1311.0-0310 135/113/168/177 1.00/3.98/2.82 CL1002+6858 41/39/57/62 1.58/6.31/4.47 RXJ003033.2+261819 47/32/57/60 1.78/7.08/5.01 MACSJ2214.9-1359 79/58/99/104 1.12/4.47/3.16 MACSJ0911.2+1746 71/49/81/88 1.00/3.98/2.82 MACSJ0257.1-2325 78/56/95/106 1.26/5.01/3.55 V1525+0958 71/52/87/94 1.00/3.16/2.51 CL1357+6232 70/61/93/97 0.89/3.55/2.51 MACSJ0454.1-0300a 66/45/75/85 2.24/7.94/6.31 MACSJ1423.8+2404a 103/83/121/127 1.41/7.08/4.47 MACSJ1149.5+2223 80/65/99/107 2.24/7.94/6.31 MACSJ0717.5+3745 127/92/141/156 1.00/3.98/2.82 MS0015.9+1609 90/78/109/116 0.63/2.51/1.78 V1121+2327 108/90/131/135 0.63/2.51/1.78 CL0216-1747 98/74/114/123 0.89/5.01/3.55 MACSJ0025.4-1222 142/111/168/182 0.89/4.47/2.82 CL0956+4107 90/65/112/118 1.00/3.98/2.82 MACSJ2129.4-0741 85/67/101/106 1.12/4.47/3.16 CL0328-2140 71/66/95/100 0.89/3.55/2.51 MACSJ0647.7+7015 68/50/89/91 1.12/4.47/3.16 CL1120+4318 56/38/70/74 1.58/6.31/4.47 CL1334+5031 49/45/70/70 2.00/7.08/5.01 CLJ0542.8-4100 113/72/127/136 0.89/3.16/2.24 CL1202+5751 77/72/102/108 0.79/3.16/2.24 CL0405-4100 82/68/101/112 1.26/5.62/3.98 MACSJ0744.8+3927 104/95/130/139 0.71/3.16/2.24 V1221+4918 103/95/139/149 0.63/2.51/1.78 CLJ0152.7-1357 95/65/116/121 1.12/3.98/2.82 CLJ1226.9+3332a 109/84/128/131 1.00/4.47/3.55

102 4.7. THE FINAL X-RAY AGN SURVEY

Figure 4.3: The sensitivity map for MACS J1931.8-2634 in the 0.5-8.0 keV energy band, calculated by solving for the number of counts required to detect a source at a binomial probability 3 of Pb= 10− . These count estimates were converted to a physical flux assuming an absorbed power-law spectrum with a photon index of Γ = 1.4. For this particular cluster, the minimum full-band flux to which 50% of the area is sensitive is 1.8 10 15 erg cm 2 s 1 . The dark region × − − − at the center of the cluster shows that the local flux limit near the cluster is approximately 5-10 times higher than the surrounding region despite being the region with the sharpest PSF, due to the presence of the bright cluster “background” emission.

103 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

Figure 4.4: The Chandra full-band counts image for the galaxy cluster MACS J1931.8-2634 (Ehlert et al., 2011), with the initial and final point source catalogs overlaid. The magenta ellipses correspond to the initial point sources detected by WAVDETECT, while the blue circles 3 correspond to point sources that satisfy the threshold Pb < 10− in the full band. The radius of the blue circle corresponds to 1.5 times the 99% EEF radius for the point source position. For this cluster, 162 initial candidate sources were detected by WAVDETECT. This was refined to 3 a final sample of 137 high-confidence (Pb < 10− ) sources detected across all three bands.

104 4.7. THE FINAL X-RAY AGN SURVEY ) 2

Full Band Soft Band Hard Band Survey Area (deg 0 5 10 10−15 10−14 10−13 S(erg cm−2 s−1) Figure 4.5: Survey solid angle as a function of ﬂux limit in the soft, hard, and full bands for all cluster observations. In order to ensure survey completeness, we have only included the region within 15 arcminutes of the aimpoints in calculating the survey area presented here. The total survey area within 15 arcminutes of the aimpoints over these 135 clusters is 12.1 deg2.

105 CHAPTER 4. PRODUCING X-RAY POINT SOURCE CATALOGS

106 Chapter 5

The Population of X-ray AGN in Galaxy Clusters: Results from the X-ray Data

5.1 Introduction

This chapter discusses the first key results of the X-ray catalogs and sensitivity maps produced in this work, which not only determines the average number of X-ray AGN we expect from the cluster, but also demonstrates the robustness of this analysis when compared to deep field surveys of X-ray AGN. Our final source catalogs and sensitivity maps allow us to determine the distribution of X-ray bright point sources across the cluster fields. We present three main results in this study: the cumulative number counts of point sources per unit sky area across the fields; their radial distributions; and tests for evolution in both the overdensity and spatial distribution beyond self-similarity. In all cases, the dominant uncertainty is the Poisson uncertainties in the total number of sources. The expected Poisson fluctuations for a sample of size n is estimated using the 1-σ asymmetric confidence limits of Gehrels (1986). The 1-σ upper confidence limit λU and 1-σ lower confidence limit λL for a sample of n sources are approximated as

λU (n) = n + 1 + √0.75 + n 1 1 3 λL(n) = n 1 Ã − 9n − 3 √n! which are both accurate within a few percent for all values of n. These Poisson uncertainties, which are at the 5% level, dominate over uncertainties in the source ﬂux measurements and uncertainties ∼ 107 CHAPTER 5. RESULTS UTILIZING X-RAYS

in the sensitivity maps. 1 Our results do not strongly depend on the choice of photon index for the canonical AGN source. In all instances, we compare our results to the results from both the CDFS and the Chandra COSMOS survey (e.g. Elvis et al., 2009; Puccetti et al., 2009) in the same energy band. These surveys should provide estimates of the number density of field sources not associated with the clusters, at least within the uncertainties associated with cosmic variance (e.g. Lehmer et al., 2012). We note that the COSMOS results presented here are obtained from re-analyzing the Chandra COSMOS field data using our pipeline. This ensures that discrepancies between the clusters and field surveys due to differences in the analysis procedure and calibration are minimal. We omit comparisons to published results based on the XMM-Newton COSMOS survey (e.g. Cappelluti et al., 2009) due to systematic differences in flux calibration, which are estimated at the 10% level in ∼ the soft band (Nevalainen, David & Guainazzi, 2010; Tsujimoto et al., 2011). Such systematic uncertainties in the source fluxes lead to systematic uncertainties in the cummulative number counts at a given flux of approximately 30%. Additionally, Chandra and XMM-Newton calibration ∼ differences are energy dependent, and will affect the hard and soft bands differently. Only a small number of sources in the final catalog have measured full-band fluxes below the flux limit at their respective positions ( 3.6%). In the majority of these cases, the flux measure- ∼ ments are consistent with the flux limits within statistical uncertainties. Only 1.2% of the point sources in the final catalog have a measured flux inconsistent with the local flux limit at their respective positions at a level greater than 68% confidence.2 It can be expected that some sources will have such characteristics, given differences in the spectra of the point sources relative to the assumed AGN spectrum (a power-law with photon index of Γ = 1.4). Indeed, those sources that are fainter than their local flux limits for which successful spectral fits were obtained are measured to be significantly softer than our canonical AGN source. The sources with fluxes below their local sensitivity limits have spectral fits that are significantly softer than the canonical Γ = 1.4, with a median value of Γ 2. General information about the point source catalogs and sensitivity maps ∼ for each cluster can be found in Table 4.2. The total survey area sensitive to different flux levels is shown in Figure 4.5. In order to ensure that our sample is reliably complete, we restrict ourselves to the central 12 arcminutes of each field of view.

1This has been confirmed by Monte Carlo simulations, calculating the logN-logS number counts from random realizations of the source fluxes and sensitivity maps. The uncertainties on the source fluxes were taken from our spectral fits, while the uncertainties in the sensitivity maps were assumed to be 20% and applied coherently across an entire field of view. Both effects were shown to contribute negligibly∼ to the overall uncertainty when compared to the Poisson fluctuations on the detected sources. 2Uncertainties on the source fluxes are estimated at the 30% level. This is the typical uncertainty in the flux measured from spectral fits to these sources using XSPEC∼ .

108 5.1. INTRODUCTION

5.1.1 Cumulative Number Counts

The cumulative number density of sources above a given ﬂux (S ) is calculated as

1 N(> S ) = (5.1) Ωi SXi>S

th where Ωi is the total survey area sensitive to the i source flux S i. The log N log S cumulative − number counts for sources in the full, soft, and hard energy bands are shown in Figure 5.1, together with a comparison to the CDFS and COSMOS results in the same bands (Lehmer et al., 2012). The cumulative number counts for both the cluster and fields sources show the commonly observed broken power-law shape (e.g. Cowie et al., 2002; Moretti et al., 2003; Bauer et al., 2004; Lehmer et al., 2012). Above fluxes of 10 14 erg cm 2 s 1 , the cluster fields exhibit a slight excess in source ∼ − − − density compared to the field surveys. These results are consistent with and build on those discussed in Paper I, and demonstrate the robustness of this analysis procedure. The final log N log S number − counts curves are shown in Figure 5.1.

5.1.2 The Radial Distribution of X-ray Sources

The spatial distribution of point sources about the cluster centers has been calculated for all point sources with full-band fluxes above 5 10 15 erg cm 2 s 1 . Similar analyses were performed in × − − − the soft band and hard bands, with flux limits of 3 10 15 erg cm 2 s 1 and 10 14 erg cm 2 s 1 , × − − − − − − respectively. The full band flux limit corresponds to a luminosity of 1042 erg s 1 for the low- ∼ − est redshift cluster in this sample (Abell 2163) and 1043 erg s 1 for the highest redshift cluster ∼ − (CLJ1226.9+3332). The adoption of these flux limits minimizes complications due to residual incompleteness and systematic uncertainties in the sensitivity maps, while maintaining a strong statistical signal. A total of 6443, 3055, and 2933 sources satisfy these criteria in the full, soft, and hard bands, respectively. The projected radial distributions are plotted in Figure 5.2 as a function of the overdensity radii, r500. The projected radii of sources in each cluster field were calculated assuming that they lie at the cluster redshift. The projected source density profile and its statistical uncertainties in each radial bin are calculated in an identical manner to that used to calculate the cummulative number counts. In this representation, we find clear evidence for an excess of point sources in the central regions of the clusters. At large radii, the measured source number densities converge to an approximately constant source density. Fitting the number density of full (0.5 8.0 keV) band sources between 3-5 − 109 CHAPTER 5. RESULTS UTILIZING X-RAYS

Table 5.1: Flux limits for the radial profiles and the expectations for the X-ray point source density from CDFS and COSMOS deep fields in all three energy bands. The columns list: (1) the energy band; (2) the flux limit used in constructing the radial profile fits, in units of 2 1 erg cm− s− ; (3) the density of field sources from the CDFS at that flux limit; (4) the density of field sources from COSMOS at that flux limit; (5) the measured background density between 3 and 5 r500 from the radial profile; (6) the number of sources detected within 2r500 at that flux 2 limit, across all clusters; (7) the survey area within 2r500 at that flux limit, in units of deg− ; (8) the average excess number of sources per cluster above that flux limit within 2r500, determined by extrapolating measurements of the field density from the best-fit constant model between 3 and 5 r500; and (9) the best-fit power-law index for the projected source density of cluster member AGN.

(1) (2) (3) (4) (5) (6) (7) (8) (9) Band Flux CDFS COSMOS Background n2 Ω2 Excess β 15 Full 5 10− 690 75 672 29 632 22 4136 6.1 2.7 0.6 0.42 0.12 × 15 ± ± ± ± − ± Soft 3 10− 250 45 255 18 263 14 2683 8.8 2.8 0.4 0.53 0.22 × 14 ± ± ± ± − ± Hard 1 10− 220 45 287 19 244 14 2595 8.5 3.1 0.4 0.48 0.14 × ± ± ± ± − ±

2 r500 with a constant model provides an estimated background number density of 632 22 deg− . ± 3 The measured value is also in good agreement with the expected background source density from the CDFS and COSMOS studies within statistical uncertainties. Within the projected central virialized cluster region ( 2r500), the constant background density model provides a poor fit to the ∼ point source density, and can be rejected at > 99.99% confidence. Most published studies of the optical galaxy population in clusters measure the projected galaxy density profile to follow a King

Model or NFW model with a scale radius of 0.2 0.5 r500(e.g. Popesso et al., 2007; Budzynski ∼ − et al., 2012, , Paper II). Such models can be rejected at high confidence for the X-ray sources, which have a measured scale radius of 1 1.5r500. The results of the background fits in all three bands ∼ − are shown in Table 5.1. The high statistical precision of our data enable us to robustly measure an excess of approximately 3 sources per cluster field within 2 r500 in each energy band. We have fitted the observed X-ray point source density profiles in all three bands with a King- law+Constant model: where rc is the core radius of the fit. A Markov Chain Monte Carlo (MCMC) analysis was used in order to determine confidence intervals on the model parameters for this analysis. We assumed a

3The constant model provides a statistically acceptable ﬁt to the data (χ2 = 4.7 for ν = 7 degrees of freedom).

110 5.2. THE LUMINOSITY OF CLUSTER MEMBER AGN

uniform prior on the core radius between 0.05 < rc < 2 r500, and sampled over a range of expected

ﬁeld source densities, CX, using a Gaussian prior based on the COSMOS results with a relative width of σ = 10%4. The resulting posterior distributions for the ﬁts in each energy band are nearly identical to one another. In each case, we measure a median core radius of rc = 1.1 r500, with a

68% confidence interval spanning the range of rc [0.8, 1.5] r500. King models with core radii ∈ rc < 0.5 r500 can be rejected at & 99% confidence. Fitting the observed X-ray point source density β profile to a power-law model (NX(r) r ) gives similar results as in Paper I: we measure a median ∼ power-law index of β = 0.5 0.1 consistently across all three energy bands. As will be discussed − ± in detail in Chapter 6, this is typically broader than what is measured for the the spatial distribution of galaxies in massive galaxy clusters. The projected density profile of optically selected cluster galaxies is usually well fit to a King model that has a core radius of rc = 0.24 0.02 r500 (Popesso ± et al., 2007), while NFW fits usually find a concentration parameter of c 2 3 (e.g. Carlberg et al., ∼ − 1997; Budzynski et al., 2012).

5.2 The Luminosity of Cluster Member AGN

We have also determined the radial distribution of X-ray point sources above the field using a full band luminosity limit of L 3 1043 erg s 1 . This is the minimum luminosity for which our ≥ × − entire survey area is sensitive for all clusters, which minimizes complications due to incompleteness or systematic uncertainties in the flux survey area across the field of view. In order to compare the number densities of each cluster field at different flux limits, we use our measurements of the COSMOS field to perform a statistical subtraction of the background field sources expected at the flux limit corresponding to this luminosity limit in each radial bin for each cluster. The average number of excess sources above the field brighter than this luminosity limit per cluster, as a function of radius, is shown in Figure 1.5(a). We measure roughly 1 source per cluster brighter than this luminosity limit within 2r500. We have also calculated the projected density profile of excess ∼ sources above this luminosity limit, shown in Figure 1.5(b). The projected source density of the most luminous AGN in clusters is well-fit by the same power-law model as that determined for the flux-limited sample, with an index of 0.5 0.6. ∼ − −

4This value for σ is sufﬁciently large to account for both the statistical ﬂuctuations in the survey source counts and the systematic uncertainty due to cosmic variance.

111 CHAPTER 5. RESULTS UTILIZING X-RAYS

Full Band (0.5−8.0 keV)

Cluster Fields CDFS COSMOS −2 N (> S) deg 10 100 1000

10−14 10−13 S (erg cm−2 s−1) (a)

Soft Band (0.5−2.0 keV) Hard Band (2.0−8.0 keV)

Cluster Fields Cluster Fields CDFS CDFS COSMOS COSMOS −2 −2 N (> S) deg N (> S) deg 10 100 10 100

5×10−15 10−14 2×10−14 5×10−14 10−14 2×10−14 5×10−14 10−13 S (erg cm−2 s−1) S (erg cm−2 s−1) (b) (c)

Figure 5.1: Cumulative number counts (log N log S ) in the full (0.5 8.0 keV, a), soft − − (0.5 2.0 keV, b), and hard (2.0 8.0 keV, c) energy bands for the cluster fields (black). The − − red curves show the cumulative number counts in the same energy bands for the CDFS (Lehmer et al., 2012). The blue curves are the results from the COSMOS survey. The band-specific flux limits used to determine the radial distribution of X-ray point sources are denoted by the vertical 14 2 1 dashed line in each figure. In all three bands an excess of sources at fluxes & 10− erg cm− s− with respect to the control fields is observed.

112 5.2. THE LUMINOSITY OF CLUSTER MEMBER AGN

Full Band (0.5−8.0 keV) −2 ) deg −1 s −2 erg cm −15 600 800 N ( > 5 x 10

0 2 4

Radius (r/r500) (a)

Soft Band (0.5−2.0 keV) Hard Band (2.0−8.0 keV) −2 −2 ) deg −1 ) deg s −1 −2 s −2 erg cm erg cm −15 −14 N ( > 10 200 300 400 N ( > 3 x 10 200 300 400

0 2 4 0 2 4

Radius (r/r500) Radius (r/r500) (b) (c)

2 Figure 5.2: The projected density of X-ray bright point sources in all three bands, in units of deg− . In all three lines, the solid black line corresponds to the best-fit constant background density in the range 3-5 r500, and in all three cases this background density is consistent with the expected field source density derived from CDFS and COSMOS. In all three energy bands, this constant background field density is consistent with the expected field density determined from the CDFS and COSMOS 15 2 1 data. (a): The surface density of X-ray bright full band sources (FX(0.5 8.0 keV) > 5 10 erg cm s ) as a function of − × − − − radius, in units of r500. A total of 6443 sources were included in the calculation of this profile. (b): The surface density of 15 2 1 X-ray bright soft band (FX(0.5 2.0 keV) > 3 10− erg cm− s− ) sources as a function of radius, in units of r500. A total of 3055 sources were included− in the calculation× of this profile. (c): The surface density of X-ray bright hard band sources 14 2 1 (FX(2.0 8.0 keV) > 10− erg cm− s− ) as a function of radius, in units of r500. A total of 2933 sources were included in the calculation− of this profile.

113 CHAPTER 5. RESULTS UTILIZING X-RAYS ) −2 (L) > 43.5, deg 10 0 50 100 −50 Excess Density (log 0 2 4

Radius (r/r500) Figure 5.3: The projected density of excess X-ray point sources detected above a full band 43 1 2 luminosity limit of L 3 10 erg s , in units of deg− . This projected source density follows ≥ × − a similar power-law model as that observed for the ﬂux-limited sample.

5.3 Evolution of Cluster AGN Beyond Self-Similarity

With a total of 135 galaxy cluster fields and more than 11000 X-ray point sources identified, our survey has sufficient statistics to examine how the X-ray AGN population depends on various cluster properties. All results discussed in this section correspond to the full band. We utilize an MCMC analysis procedure to determine posterior probability distributions for parameters in a redshift, luminosity, and mass dependent model for the projected source density profile. Our model assumes that the projected number density of cluster sources above a given flux limit f for a cluster at a redshift of z and projected distance of r follows a separable power law model:

D2 r β N (> f, r) = 4r A N Φ(> L , z) + C (5.2) obs 500 2 0 cut 57.296 × × × Ãr500 ! where Φ(> L, z) is the integral of the X-ray luminosity function (XLF) above our given luminosity 46 1 limit Lcut, up to a maximum luminosity of Lmax = 10 erg s− . The lower limit of the luminosity function, Lcut, is the intrinsic luminosity of an AGN at the cluster redshift corresponding to the cutoff flux f in our survey, in this case 5 10 15 erg cm 2 s 1 .The background constant C corresponds to × − − − the expected population of field galaxies at this same flux limit. We assume that this model is valid within a cube with a length of 4r500, centered on the cluster center. The parameter N0 in our model

114 5.3. EVOLUTION OF CLUSTER AGN BEYOND SELF-SIMILARITY

corresponds to the overdensity factor of AGN in clusters with respect to the field. For the XLF, we assume the Luminosity-Dependent Density Evolution (LDDE) model of Ueda et al. (2003), fit in the rest frame energy band of 2 10 keV. − The XLF of Ueda et al. (2003) was determined in the rest frame 2 10 keV band, while we are − using the 0.5 8.0 keV band in order to maximize the statistics of our measurement. In order to − account for this energy band conversion, we convert the relevant parameters of the Ueda et al. (2003) model (L⋆ & La) to the full band assuming a power-law photon index of Γ= 1.4. Additionally, we allow our priors to have statistical uncertainties a factor of 2 larger than the error bars published in Ueda et al. (2003), in order to account for the fact that the XLF may take on different shapes in these two energy bands. However, the majority of the parameters for this model of the XLF are consistent with those measured in softer energy bands (e.g. Hasinger, Miyaji & Schmidt, 2005), suggesting that this procedure should not introduce any additional systematic errors in our analysis. We allow for redshift and mass evolution in the AGN population by allowing the overall cluster overdensity N0 to vary with redshift and mass as power-laws with a free logarithmic slopes of η and ζ, respectively ζ η M500 N0 N0(1 + z) (5.3) → Ã1015 M ! ⊙ and determine the posterior probability distribution for η and ζ. Under our null hypothesis of self- similar evolution, we expect that clusters host an overdensity of AGN with respect to the field that otherwise evolves in the same manner as in the field. Consequently, evidence for evolution in clus- , , 1 ters beyond self-similarity requires statistically measurements of η 0 and ζ 3 . We take these parameter values as our null hypothesis.5 In an addition to these terms that allow the overall overdensity with respect to the field to vary with cluster mass and redshift, the radial distribution of AGN in clusters is also allowed to vary with cluster mass and redshift, by including first order terms to the

M500 radial power-law index (β β0 + βz(1 + z) + βm ). Statistically signiﬁcant measurements of → 1015 M ³ ⊙ ´ these terms having non-zero values (i.e. βz,m , 0) would suggest deviations from self-similar evolution in the spatial distribution of the cluster AGN. One additional prior is necessary: the background source density expected at our chosen ﬂux limit, which we determine from the stacked density pro-

file in the range of 3 5r500. This background field density is parameterized as a Gaussian whose − 5Since our model already accounts for the redshift dependence of the comoving density of field AGN, η measures how the cluster-specific overdensity evolves with redshift. In self-similar evolution, η should have a value of 0. We also expect that the overdensity factor should not depend on cluster mass. However, by projecting the 3-d source density along the line of sight, our measured source density profile should have a 1/3 weak mass dependence. Since r500 scales as M500, we expect that our projected density profile as modeled above will have ζ = 1/3 in absence of any peculiar∼ evolution.

115 CHAPTER 5. RESULTS UTILIZING X-RAYS

mean density and its variance are determined from the stacked source density proﬁle in the range of 3 5r500. The ﬁnal ingredient for this MCMC analysis is the likelihood function ( ), and is the − L Poisson probability of detecting the observed number of sources in each cluster and each radial bin

P Cij Nobs(> f, r) Ωij (5.4) L∝ | × Yi, j ³ ´ where Nobs(> f, r) includes all of the terms discussed above and Cij & Ωij are the number of detected sources and the survey area for the ith cluster’s jth radial bin, respectively. This MCMC analysis provides several key advantages over a more traditional statistical analysis, in particular: 1) It uses the full information of cluster redshifts and masses without the need to resort to binning; 2) we are able to straightforwardly interpret the results within the context of our complex selection function, which varies the overall luminosity limit from cluster to cluster as well the area in each radial bin sensitive to sources of a given ﬂux; and 3) We are able to robustly determine the covariances between the different model parameters, which are difﬁcult to anticipate a priori. We marginalize over all XLF uncertainties using Gaussian priors motivated by the measured LDDE model parameters in

Ueda et al. (2003), and initially sample over uniform distributions for the cluster normalization N0, the radial power-law slope β, and the redshift evolution power-law slope η. We perform three separate runs of our MCMC analysis to ensure that we are fully sampling the covariances between the parameters: The ﬁrst run assumes a single power-law slope for all

135 clusters (βz, βm = 0) in order to determine the most precise constraints on η, ζ (hereafter the normalization model); while the second run assumes that the overdensity evolves in a self-similar manner (η = 0, ζ = 1/3) in order to determine the most precise constraints on βz, βm (hereafter the radial model). The third run (hereafter the full model) marginalizes over the posteriors of all four of these parameters simultaneously. We only claim that parameters deviate from the self-similar predictions if the full model and its precise model are consistent with one another and inconsistent with their respective null hypotheses at high statistical confidence. We summarize the input priors and 1-dimensional output posterior distributions in Table 5.2. We find that, in most cases, the majority of our parameters are consistent with their respective null hypotheses. In particular, there is no evidence from our data that the spatial power-law terms (βz and βm) differ from 0 with any meaningful statistical significance. The same is true for η, which is not well constrained by our modeling. However, we measure a value for the mass scaling of the overdensity, ζ = 0.67 0.10 in both the precise and full models. The null hypothesis of − ± ζ = 1/3 can be rejected at high confidence (> 99.99%). After accounting for the projection effect

116 5.3. EVOLUTION OF CLUSTER AGN BEYOND SELF-SIMILARITY

Table 5.2: Results of our MCMC analysis to determine the posterior probability distributions of evolutionary parameters in our projected source density profiles. Top: Input priors on the XLF after converting published results to our energy band. All of the priors with error bars are assumed to be normally distributed, while those without error bars were assumed to be fixed. Our priors have error bars a factor of 2 larger than the published values in order to account for any potential systematics that may arise in the energy band conversion. Bottom: The resulting parameter values from our posterior probability distributions for terms beyond self-similarity. For each parameter, we show the median value and its 68% confidence interval, as determined by the 1-dimensional posterior probability distributions. All posterior distributions were determined simultaneously using an MCMC analysis that either accounts for the normalization and slope terms separately (the normalization and spatial models, respectively), or in one that accounts for all four parameters simultaneously (the full model). We find that one parameter shows statistically significant deviations from our self-similar prediction: ζ, the scaling of the overdensity with mass.

XLF Priors Parameter Prior 6 A0 (5.04 0.66) 10 ± × − γ1 0.86 0.30 ± γ2 2.23 0.26 ± log L⋆ 43.94 0.52 ± p1 4.23 0.78 ± p2 1.5 − zc∗ 1.9 log La 44.6 α 0.335 0.14 ± Measured Posteriors Parameter Full Model Spatial Model Normalization Model +1.57 η 5.9 2.05 0 0.32 2.3 − ± ζ 0.67 0.09 0.33 0.69 0.09 − ± − ± +0.30 +0.74 β0 0.96 0.43 0.65 1.14 0.52 0.07 − − − − − ± βz 0.33 0.23 0.73 0.89 0 ± − ± βm 0.03 0.07 0.37 0.14 0 ± ±

(i.e. the multiplication of the XLF by 4 r500 to account for the line-of-sight depth), such a value for 1 ζ suggests that the comoving overdensity of AGN in clusters scales with cluster mass as M500− .

117 CHAPTER 5. RESULTS UTILIZING X-RAYS

118 Chapter 6

The Population of X-ray AGN in Galaxy Clusters: The Fraction of Galaxies Host- ing X-ray AGN

6.1 Introduction

Although the previous sections demonstrate that the galaxy clusters have a clear excess of sources when compared to expectations from field AGN surveys, this result in and of itself is hardly sur- prising given the significant overdensities of galaxies in these fields with respect to the field. Deter- mining the true extent to which the cluster environment influences the evolution of AGN requires a way to account for the underlying galaxy population. A joint X-ray and optical study is therefore ideally suited to determine more precisely how the cluster environment may transform galaxies and their central black holes. By understanding how relative rates of galaxies hosting AGN vary throughout the cluster region, we are able to disentangle the effects of multiple physical processes on galaxy evolution and establish important connections between X-ray AGN, optical AGN, and star formation in cluster member galaxies.

6.1.1 Additional Limitations

Chapter 4 describe the processes by which the X-ray point sources in the ﬁelds were identiﬁed, using a procedure similar to the most recent iteration of the Chandra Deep Field South (CDFS, Xue et al., 2011). For the purposes of this chapter, however, we further limit our X-ray point source catalogs to those sources that satisfy: 1) Are hosted in clusters that have both deep Chandra X-ray observations

119 CHAPTER 6. JOINT X-RAY AND OPTICAL RESULTS

and deep multi-filter optical imaging observations with either Subaru or CFHT in hand1; 2) A full 14 2 1 band (0.5 8.0 keV) flux brighter than FX(0.5 8.0 keV) > 10 erg cm s ; 3) Source positions − − − − − within 8′ of the exposure weighted mean aimpoint of the Chandra observations; and 4) an effective Chandra exposure time at the source position of at least 20 ks (see Chapter 4 for details). Every source that satisfies these two criteria will have 15 net counts, which for Chandra amounts to a ∼ 6 detection with a false-positive probability of Pb . 10− . In total, the final X-ray catalog has 571 X-ray point sources, the vast majority of which will be AGN. For the sources satisfying these three criteria, we expect the point source catalog to have near 100% completeness and purity. The positional uncertainties for our X-ray sources are expected to be no larger than 1.0 arcsec (Xue et al., 2011). Eight of the galaxy clusters with X-ray and optical ∼ imaging data have a nominal Chandra exposure time less than 20ks, and the X-ray source catalogs for these 8 clusters do not contribute to this work.

6.1.2 Optical Catalog Production

The optical imaging which used to identify stars and galaxies in these cluster fields is described in detail by the Weighing the Giants project, von der Linden et al. (2012, herafter WtG1), Kelly et al. (2012, WtG2), and Applegate et al. (2012, WtG3). For each cluster, deep imaging taken with SuprimeCam at the Subaru telescope and/or MegaPrime at the CFHT is available in at least three filters. Here we use the object catalogs described in Sect. 6.2 of WtG1 - these were produced with SExtractor parameters suitable for identifying larger objects, such as the galaxies in the clusters described here (in contrast to the weak lensing catalogs used primarily in the Weighing the Giants project, which require shape measurements of faint, small galaxies). The optimization for larger objects comes at the cost of a somewhat higher incompleteness at faint magnitudes; however, we find that all fields are highly complete at least to R < 24 in the SuprimeCam R-band. In order to compare the observed AGN fractions to those inferred from COSMOS, we use aperture magnitudes with 3 arcsec diameter. For three fields which were were observed with MegaPrime r-filter, we convert the magnitudes into SuprimeCam R-band using the empirically determined correction formula from Lupton2 R = r 0.1837 (g r) (6.1) − × − 1A subsample of 43 galaxy clusters in the parent sample of 135. 2This particular conversion is discussed at http://www.sdss.org/dr5/algorithms/sdssUBVRITransform.html, and we have verified its accuracy on cluster data sets which have been imaged with both CFHT and Subaru filters.

120 6.1. INTRODUCTION

We distinguish between galaxies (extended objects) and stars (optical point sources) based on the FWHM and the SExtractor CLASS STAR parameter, which are both measured on the detection image (the image with the best seeing, see WtG1). In 6 ﬁelds, the BCG is saturated in the detection image - in these cases we based the magnitude limits for the catalogs on images in different bands that are not saturated.

6.1.3 COSMOS as a Control Field

In order to determine the expected properties of the field galaxy population, we utilize X-ray and optical source catalogs from the Cosmic Evolution Survey (COSMOS). The X-ray source catalog for the Chandra COSMOS survey field was produced using the same procedure used for the cluster fields, while the COSMOS optical catalog utilized in this study is from Capak et al. (2007). We utilize the observed magnitudes in all 30 COSMOS filters to determine their Subaru R-band magnitudes, using the photometric redshift calculations discussed in WtG2.

6.1.4 Counterpart Matching

For our counterpart matching between X-ray and optical source catalogs, we use a fixed 2 arcsec matching radius. This matching radius maximizes the number of X-ray sources with optical counterparts while still maintaining an acceptably low rate of expected chance matches. The rate of chance matches was determined by adding offsets to the X-ray source positions and re-running our counterpart matching; we estimate from this calculation that 10% of the matches may be due to chance ∼ coincidence. This matching radius is sufficiently large to account for both positional uncertainties in the X-ray sources due to e.g. the variations in the Chandra point spread function across the field of view and uncertainties in the overall Chandra astrometric solutions. As discussed in WtG1, the astrometric solutions for the Subaru data have absolute precision at the level 0.1 arcsec or better, ∼ and the positional uncertainties for our optical data can therefore safely be neglected for this analysis. In the few instances where more than one optical source was located within the matching radius of an X-ray source, the brightest optical source was chosen as the counterpart. Our matching rates for the chosen X-ray and optical flux limits are similar to those determined with deep field surveys such as COSMOS, when operating in the same wavebands and flux ranges. After performing the matching procedure on the full X-ray and optical catalogs, we select those X-ray sources with optical counterparts that also satisfy the following conditions: (1) the optical counterpart is a galaxy brighter than an R-band magnitude of 23; and (2) the optical counterpart is

121 CHAPTER 6. JOINT X-RAY AND OPTICAL RESULTS

fainter than RBCG 0.5, where RBCG is the R-band magnitude of the BCG for the particular ﬁeld. − We exclude the BCG’s themselves from our standard analysis. Because they are subject to different physical processes than typical cluster member galaxies, we will discuss them separately.3 Tests carried out on X-ray sources matched to other optical source populations (discussed in more detail below) show no evidence for cluster member AGN hosted in faint galaxies with R > 23 (i.e. excess sources above the expected ﬁeld counts located in the clusters). Additionally, no cluster member AGN are detected in X-ray sources matched either to optical stars or in X-ray sources without optical counterparts. We therefore conclude that the bulk of the cluster member X-ray AGN are hosted in galaxies with R < 23.

6.1.5 Luminosity and Stellar Mass Limits

Our study utilizes flux-limited samples in lieu of absolute magnitude or stellar mass limited samples in order to maximize the sample sizes and hence the statistical power of the study. At the optical flux limit discussed above (R < 23), this flux limit corresponds to an absolute magnitude limit of MR 20 for the highest redshift cluster in this sample (MACS J0744.8+3927, z = 0.697). ∼ − This absolute magnitude roughly corresponds to a stellar mass of 1010 M . Given our overall ∼ ⊙ flux limit, the X-ray AGN sample is complete for all redshifts considered down to a luminosity of 43 1 LX 1.6 10 erg s . For our lowest redshift cluster (Abell 209, z = 0.206), these same flux ∼ × − 42 1 limits correspond to MR 17 and LX 1.1 10 erg s . ∼ − ∼ × − Although photometric redshifts of background galaxies in a subsample of these clusters are presented in WtG2, identifying galaxies at the cluster redshifts with photometry is still subject to large uncertainties, in particular for identifying blue or faint galaxies (Rudnick et al., 2009). Therefore any cluster AGN fractions calculated using photo-zs would be subject to large systematic errors. To avoid such complications and provide the most statistically precise signal possible, we do not utilize photo-z’s here.

3The BCG’s in this cluster sample have R-band magnitudes in the range of R = 17.7 20.9, with a BCG − median value of RBCG = 19.3. We caution that since these are aperture magnitudes they will systematically underestimate the actual BCG ﬂuxes, given that most BCG’s are larger than 3 arcsec. The choice of using aperture magnitudes is motivated by the COSMOS catalogs, for which only aperture magnitudes are public.

122 6.2. RESULTS −2 ) deg −1 s −2 erg cm −14 > 10 X N ( F 0 100 200 0 1 2

Radius (r/r500)

Figure 6.1: The projected number density profile of X-ray point sources with FX(0.5 14 2 1 − 8.0 keV) > 10− erg cm− s− with optical counterparts located within 2 arcsec of the X-ray source position. Radii are scaled in units of r500. The magnitude range for the optical counterparts is RBCG 0.5 < R < 23, where RBCG corresponds to the magnitude of the BCG for each − individual cluster. Any X-ray point sources matched to BCG’s are not included in this profile. The projected source density profile beyond r500 is consistent with COSMOS at the 95% ∼ confidence level. We caution, however, that the X-ray source density measured by COSMOS may be slightly overdense with respect to the cosmic mean. The maximum number of X-ray point sources in a radial bin is 26, and the minimum number is 6.

6.2 Results

6.2.1 The Projected Density of Cluster X-ray Sources

We use the procedure discussed in Paper I in order to determine the projected density proﬁle for X- ray point sources. We omit any X-ray sources located in masked regions of the optical images, e.g. at the positions of bright stars. Figure 6.1 shows the projected density proﬁle of X-ray point sources, 14 2 1 NX(FX > 10 erg cm s , r), matched to optical galaxies with RBCG 0.5 < R < 23. The total − − − − 123 CHAPTER 6. JOINT X-RAY AND OPTICAL RESULTS

number of X-ray point sources that satisfy all of our previously stated selection criteria is 148. An excess of sources is observed within the central r500, but beyond this radius the source density ∼ proﬁle converges to a constant value consistent with expectations from the ﬁeld (see below). These results are consistent with those of Paper I, although the stricter selection criteria employed here (especially the optical counterpart matching) reduces the sample size and ultimately the statistical precision which we can measure excess source densities associated with the clusters.

Measuring the projected source density profile out beyond 1.5r500 gives a measured field density 2 (92 13 deg− ) slightly lower than, although formally consistent with the expected field density ± 2 from COSMOS (120 12 deg− ). We note that reconstructions of the COSMOS density field using ± deep spectroscopic measurements have determined a significant overdensity with respect to the cosmic mean at redshifts of z 0.8 1.0(Kovacˇ et al., 2010), which is also the peak of the redshift ∼ − distribution for X-ray AGN in field surveys (e.g Brusa et al., 2010; Luo et al., 2010; Xue et al., 2011). It is therefore possible that the X-ray point source density in the COSMOS field is systematically slightly higher than the true field source density.

6.2.2 The Projected Density of Cluster Optical Sources

Figure 6.2 shows the projected density of optical galaxies, NO(r), corrected for regions “masked” by brighter foreground objects. The total number of bright (R < 23) galaxies observed within 8′ of the Chandra aimpoints is 44,738. A clear excess of sources above the expected field density is observed out to beyond 2.5r500. At distances beyond 2.5 r500, the source density in the cluster ∼ fields is roughly 15% higher than COSMOS, which is unsurprising given that this radius is still ∼ within the turnaround radii of the clusters. The observed projected galaxy number density profile is peaked at the cluster center (excluding BCG’s) and can be well described with a King+constant model with a King law core radius of rc 0.3r500. ≈

6.2.3 The Fraction of Cluster Galaxies Hosting X-ray AGN

Figure 6.3 shows the absolute fraction of cluster+ﬁeld galaxies hosting X-ray AGN, obtained by counting the total number of X-ray point sources matched to optical galaxies in each radial bin across all of the clusters and dividing by the total number of galaxies in those same radial bins. Because the completeness of the two samples is 100%, this calculation is equivalent to the ratio ∼ between the projected source density proﬁles shown in Figures 6.1 and 6.2. The AGN fraction near the cluster centers is clearly lower than the expected COSMOS value,

124 6.2. RESULTS 5 10 −2 4 8×10 4 6×10 −0.5 < R 23) deg 4 BCG 4×10 N ( R 4

2×10 0 1 2

Radius (r/r500)

Figure 6.2: The projected density of optically selected sources (RBCG 0.5 < R < 23) in galaxy − cluster fields, as a function of radius in units of r500. The dashed line denotes the expected density of field galaxies from COSMOS, using the median BCG magnitude of RBCG 0.5 = − 18.83. A clear excess of galaxies corresponding to a 15% overdensity above the COSMOS ∼ value is observed at distances of 2.5r500. BCG’s are not included in this profile. ∼

and slowly rises with radius before converging to the COSMOS value at distances of & 2r500. In the central-most regions of the clusters (excluding the BCG’s), the fraction of cluster+field galaxies hosting X-ray AGN is roughly a factor of 3 lower than the COSMOS AGN fraction. The AGN fraction shown in Fig. 6.3 is a combination of the cluster AGN fraction and that of the field. In the very central bins, the contribution from background galaxies is modest(see Fig. 6.2), but at distances of r r500, the numbers of cluster and background galaxies are comparable to one another. Using ∼ the radial profiles of X-ray sources and galaxies, we have performed a crude statistical subtraction of the field contribution for these radial bins. We used Monte Carlo methods to subtract the field population by fitting the X-ray and optical source density profiles (shown in Figs. 1 and 2, respectively) beyond 2r500 with a constant model. This analysis confirms that the cluster AGN fraction is

125 CHAPTER 6. JOINT X-RAY AND OPTICAL RESULTS −3 8×10 −3 6×10 −3 Fraction 4×10 −3 0 2×10 0 1 2

Radius (r/r500)

Figure 6.3: The fraction of cluster+field galaxies (RBCG 0.5 < R < 23, not including BCG’s) 14 2 1 − hosting X-ray bright (FX > 10− erg cm− s− ) AGN, as a function of radius in units of r500. The dashed lines denote the field AGN fraction inferred from COSMOS at the same limits for the X-ray flux and optical flux, using the median BCG magnitude of RBCG,med 0.5 = 18.83. A − trend that rises with clustercentric radius is observed, which converges to expected field value at distances of 2r500. ∼

consistently lower than the ﬁeld fraction within r500, by a factor of 3 4. The statistical un- ∼ ∼ − certainties on this measurement are too large to constrain the gradient of the cluster-speciﬁc AGN fraction, however.

6.2.4 The Fraction of BCG’s Hosting AGN

Performing the same analysis procedure and using the same ﬂux limits discussed above, we have also calculated the fraction of BCG’s that host X-ray bright AGN. Two of the BCG’s of the 35 galaxy clusters (MACS J1423.8+2404 and MACS J1931.8-2634) in the ﬁnal sample host X-ray bright AGN. This fraction ( 6%) is a factor of 10-20 higher than the typical X-ray AGN fraction for ∼ 126 6.2. RESULTS

cluster member galaxies in the central regions of the clusters, although it is consistent with the value 12 42 1 measured for the most massive galaxies (M⋆ 10 M ) hosting X-ray AGN with LX > 10 erg s− ∼ ⊙ 43 1 in the ﬁeld (Haggard et al., 2010). Both of the AGN hosted in BCG’s are brighter than 10 erg s− . We emphasize that our selection procedure is highly conservative in identifying point sources in BCG’s and only includes the most obvious sources (see Paper I for more details). It is therefore possible that our measurement underestimates the true fraction of BCG’s hosting X-ray AGN.

127 CHAPTER 6. JOINT X-RAY AND OPTICAL RESULTS

128 Chapter 7

AGN Feedback and Ram Pressure Strip- ping: The Physical Picture

7.1 Introduction

The X-ray, optical, and radio data presented in the previous chapters demonstrate a number of different physical mechanisms by which galaxies and the surrounding ICM are transformed by one another. We now discuss the implications of our ﬁndings in the sections below. These conclusions span the results of all of the previous results presented in this thesis.

7.2 Extreme Heating by Central AGN Feedback in MACS J1931.8-2634

As a larger, more luminous, higher redshift analog to nearby systems like the Perseus Cluster (Fabian et al., 2003, 2006), MACS J1931.8-2634 provides an extreme example of a cluster with a rapidly cooling core and powerful AGN feedback. This powerful AGN outburst combined with merger-induced motion has led to a cool core undergoing destruction to an extent previously unob- served in galaxy clusters. There is clear evidence that MACS J1931.8-2634 has undergone a merger event that induced large oscillatory motions of the core. On scales of r 200 kpc, a spiral of cooler, denser gas seen ∼ in both the X-ray image and temperature map is observed to wrap around the core. Such spiral structures arise naturally from mergers and subsequent sloshing and are observable for several Gyr after the merger event (Ascasibar & Markevitch, 2006). Clear deviations from elliptical symmetry are seen in the isophotes, whose centroids shift with distance along the major axis to the north and

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south. On smaller scales near the core (r 50 kpc), the X-ray data show that the core has a history of ∼ motion in the north-south direction. There are two bright ridges to the north and south of the central AGN. The northern ridge has a sharp northern edge (possibly a cold front), while the southern ridge has a diffuse tail of emission trailing its southern edge. Both of these features are consistent with the densest gas currently undergoing motion to the north. Our optical data independently suggest such a merger: the ICL around the cD galaxy is highly elongated in the north-south direction. B-band emission originating from a young stellar population is observed both to the north and south of the cD galaxy, while Hα emission originating from ongoing star formation is observed predominantly at positions coincident with the northern ridge. The presence of a young stellar population to the south without any Hα emission suggests that the primary region of star formation is moving northward. In the midst of the motions of the cluster core, a powerful AGN outburst has taken place. The central AGN is bright in X-ray emission, with a luminosity in the energy band of 0.7-8.0 keV of 8 1043 erg s 1. This AGN is surrounded by extended, amorphous 1.4 GHz radio emission. The ∼ × − major axis of this radio emission is spatially coincident with depressions in the X-ray emission. The physical extent of the cavities based on the observations is unclear, but estimating cavities based on the X-ray and radio emission gives a robust range for the 4PV enthalpy of the cavities and their corresponding jet power. The 4PV enthalpy of these cavities is sufficient to counteract the radiative losses from the central 50 kpc for 30-250 Myr. The jet power ( 4 – 14 1045 erg s 1) identifies ∼ × − MACS J1931.8-2634 among the most powerful cavity sources yet observed. The power input into inflating these cavities is approximately 100 times larger than the measured radiative power of the central AGN, and four to ten times larger than the bolometric luminosity of the cool core. Unlike other more typical cool core clusters (Allen & Fabian, 1998; De Grandi & Molendi, 2001; Vikhlinin et al., 2005; Pratt et al., 2007; Werner et al., 2008), the azimuthally-averaged metallicity profile for MACS J1931.8-2634 shows no significant deviations from a constant value. As- suming that there once was a central metallicity peak in MACS J1931.8-2634, this suggests that large masses of metal-rich gas has been stripped from the center of the cluster and displaced to the surrounding regions. The extent of transport required to account for the flat metallicity profile is strong evidence that the original cool core has undergone destructive stripping as it traversed from one side of the cD galaxy to the other. The only region with an exceptional metallicity is the northern central ridge. The central metallicity peak in cool core clusters is usually expected to be robust, even in clusters with powerful central AGN activity (Bohringer¨ et al., 2004; Rasera et al., 2008). Merger and central AGN activity have resulted in the formation of two X-ray bright ridges

130 7.2. EXTREME HEATING BY CENTRAL AGN FEEDBACK IN MACS J1931.8-2634

roughly equal distances north and south of the central AGN. The X-ray bright ridge to the north of the central AGN has characteristics usually associated with a cool core. It is the location of the lowest temperature, highest density gas in the cluster. It is also the location of the most metal rich gas. The X-ray spectrum of this northern ridge has a spectroscopic cooling rate of M˙ 165 ∼ 1 M yr− down to 0.1 keV, in good agreement with the observed star formation rate estimated from ⊙ 1 the Hα emission, 170 M yr− . This Hα emission is expected to be almost entirely from star ∼ ⊙ formation, as it is roughly 3 orders of magnitude larger than what expected from Case B Recombi- nation (Johnstone, Fabian & Nulsen, 1987). The spectroscopic cooling contributes a large fraction of the emission from this region, estimated at 30% based on the spectral modeling. The northern ridge might therefore be in the early stages of ’catastrophic cooling’ (e.g. Fabian & Nulsen, 1977; Peterson & Fabian, 2006). The cooler ICM gas to the south of the cD galaxy has a lower metallicity and a current spectroscopic cooling rate consistent with zero, but is also the location of a young stellar population. This appears consistent with sloshing-induced stripping of the cool core throughout its oscillations along the north-south axis. The asymmetric thermodynamic structure and different stellar populations of these ridges clearly indicate that there is no longer a single core of low entropy gas surrounding the cD galaxy. The extent to which AGN feedback contributed to the present-day thermodynamic structure of MACS J1931.8-2634, apart from core sloshing, is beyond the scope of the current observations. The majority of the stripping and disruption of metal rich, low entropy gas from the original cool core could have been caused by the bulk motions, but it seems likely that the AGN outburst may have contributed to the separation of the preexisting core into two X-ray bright ridges roughly equal distances from the central AGN (Guo & Mathews, 2010). This cluster’s star formation is exceptional, especially as it does not satisfy all of the empirical conditions for central star formation discussed in Rafferty, McNamara & Nulsen (2008b). Although the cooling time and entropy are below the thresholds put forward by Rafferty, McNamara & Nulsen (2008b), all of the systems with clear evidence for high star formation rates also have AGN jet powers smaller than the cooling luminosity, which is not the case in MACS J1931.8-2634.

Although the extent of stripping and cool core disruption in MACS J1931.8-2634 is substantial, similar phenomena have also been observed in the nearby Ophiuchus Cluster(Million et al., 2010a). Many of the morphological structures seen in MACS J1931.8-2634 are also seen in the Ophiuchus Cluster. Both clusters have clearly shifting isophotes, inner cold fronts, and comet-like diffuse emission trailing to one side of the inner cool core. The Ophiuchus Cluster also shows strong evidence for stripped core gas in the form of a metal rich ridge to the north of the cool core. These processes also appear to be occurring in MACS J1931.8-2634, but with the added complications

131 CHAPTER 7. THE PHYSICAL PICTURE

and energy of central AGN feedback. The extent to which a cool core can be disrupted or even destroyed by AGN feedback and merger induced oscillations has important implications for cosmological studies with clusters (e.g. Burns et al., 2008; Mantz et al., 2009a,b). Further observations with X-ray, optical, and radio instruments could provide many new insights into the extent that this cool core has been disrupted. Radio observations at higher resolutions and lower frequencies could allow for a better understanding of the amorphous central radio source, in particular discerning the origin of the emission by measuring the radio spectrum. Higher resolution, deeper observations may also be able to resolve the jet and lobe structure of the central AGN source, which is critical for further constraining the magnitude and origin of the AGN outburst. Optical spectroscopy would enable more precise measurements of the star formation rate in the different regions surrounding the central AGN, and perhaps allow for measurements of the black hole mass. Optical spectroscopy would also allow for measurements of the emission line velocities within the cool core remnant, providing more details as to the extent of the disruption of the cool core. Deeper observations with Chandra would provide better measurements of the unusual metallicity structure and the extent that gas has been stripped from the cool core remnant. The distribution of regions that undergo cooling and the extent of that cooling could also be measured in more detail. Finally, a deeper X-ray exposure could shed new light on the thermodynamic structure of the cluster, in particular provide more compelling evidence for cold fronts and/or shock heating within the central 100 kpc. Simulations designed to reconstruct the thermodynamic structure of MACS J1931.8-2634 may elucidate the nature of extreme sloshing and feedback, and perhaps also the future evolution of such a profoundly disrupted core.

7.3 The Nature of Ram Pressure Stripping in Cluster Galax- ies in the Vicinity of M86

Using a mosaic of XMM-Newton observations of the region surrounding M86, we have identiﬁed multiple sites of stripped X-ray emitting gas associated with all four galaxies: M86, M84, and NGC 4438, and NGC 4388. Although the evidence for stripped X-ray emitting gas near NGC 4388 is weak, radio observations of this galaxy show a tail of H I gas trailing to the north of this galaxy, with a velocity structure consistent with NGC 4388 (e.g. Oosterloo & van Gorkom, 2005). All four of these galaxies are therefore undergoing some form of ram pressure stripping, although each galaxy has its own unique observational signatures associated with it. The stripped X-ray emitting

132 7.3. THE NATURE OF RAM PRESSURE STRIPPING IN CLUSTER GALAXIES IN THE VICINITY OF M86

gas trailing to the northwest of M86 and the H I tail trailing to the north of NGC 4388 have been detected and discussed in previous work (e.g. Randall et al., 2008; Oosterloo & van Gorkom, 2005). The stripped X-ray emitting gas to the south of M84 and the cool X-ray emitting gas stretching from M86 to NGC 4438 have not been discussed at length1. We expand our discussion of these two sites of ram pressure stripping below. While all four of the galaxies lie near one another in projection, the X-ray data combined with absolute velocity measurements along the line-of-sight suggest that only NGC 4438 and M86 are interacting with one another in any substantial way. There is no evidence in either the X-ray imaging or spectral maps for diffuse emission or cool gas bridging between any other pair of galaxies in a manner suggesting galaxy-galaxy interactions. The reported intracluster light (ICL) between M86 and M84 (Rudick et al., 2010) therefore appears likely to be a projection effect.

7.3.1 Correlation of the Coolest X-ray Gas and Hα Filaments

In Figure 3.5a, we show the temperature map of Figure 3.3c zoomed in to better show the 0.6 keV ∼ gas plume to the east of M86. We have overlaid the Hα filaments identified by Kenney et al. (2008), which clearly show that the Hα filaments are located at the same positions as the lowest temperature phases of X-ray emitting gas. This region, at least the half closer to M86, is also observed to be a site of HI emission (Oosterloo & van Gorkom, 2005), although based on the velocity structure of that emission it may be primarily associated with NGC 4388 to the south of M86. On the other hand, some of the H I filaments detected near M86 have velocity structure similar to M86 (Kenney et al., 2008). A similar spatial coincidence between cool X-ray emitting gas and Hα filaments has been seen in a stripped tail behind the galaxy ESO 137-001 falling into the cluster Abell 3627 (Sun, Donahue & Voit, 2007). The stripped gas trailing behind ESO 137-001 is a site of in situ star formation taking place outside of the galaxy within the ICM. A similar coincidence of cool X-ray emitting gas (at temperatures of 0.6 keV) with filaments of Hα emission (at temperatures of 104 K) has also ∼ ∼ been observed around the BCGs of nearby cool core clusters such as the Perseus Cluster (Sanders & Fabian, 2007; Fabian et al., 2008b, 2011), Centaurus Cluster (Sanders & Fabian, 2002, 2008),

1The diffuse tail of gas trailing to the south of M84 was discussed briefly in Randall et al. (2008), although there were insufficient counts in the Chandra data to resolve its thermodynamic structure. Also, a coincidence between Chandra detected X-ray filaments and Hα filaments was observed in the immediate vicinity ( 5 kpc) of NGC 4438 (Machacek, Jones & Forman, 2004), and we are able to expand on this spatial coincidence∼ to include the entire region between NGC 4438 and M86.

133 CHAPTER 7. THE PHYSICAL PICTURE

and M87 (Werner et al., 2010, 2012).

7.3.2 On The Origin of the Hα Filaments

The origin of this 104 K phase of gas spatially coincident with soft X-ray emission between M86 ∼ and NGC 4438 is likely a previous collision between the two. The existing multiwavelength data are all consistent with this scenario, although further data will be required to rigorously confirm this. The X-ray emitting gas in this filament region appears poorer in metals than the gas detected at the center of M86, implying that this cool X-ray emitting gas likely originated in NGC 4438. Measurement biases, due to the presence of multiple temperature phases unaccounted for in our models cannot be ruled out as the reason for the observed low metallicity in this region, however. In the absence of sources of heating, this stripped gas may be expected to cool to star-forming temperatures, as is observed in the galaxy ESO 137-001 in Abell 3627 (Sun, Donahue & Voit, 2007). Uncertainties in the filament geometry (specifically the depth of the filament regions along the line of sight) limit our ability to determine the cooling time of the cool gas unambiguously. Based on the combined analysis of all of the spectral bins in this filament region discussed above, we can place an upper limit on the integrated cooling rate from 1 keV down to 0.1 keV across the whole region 1 of . 0.1M yr− . This is in agreement with the upper limit on star formation imposed by far UV ⊙ emission of this same region discussed above. Spiral galaxies such as NGC 4438 typically have X-ray halos with an observed temperature of 0.2 keV, while the gas associated with the group halo of M86 has a temperature of 1 keV. The ∼ ∼ presence of 0.6 0.7 keV gas in between the two galaxies suggests a possible mixture of gas ∼ − phases. This “bridge” of cool gas between the two galaxies may arise either from ram pressure stripping of NGC 4438 by the surrounding Virgo ICM, or from the recent encounter between NGC 4438 and M86. The stripping medium in this latter scenario could either be the X-ray gas halo of M86 or the intragroup medium surrounding it, both of which could result in the observed filament of 0.6 0.7 keV gas. ∼ − The spatial coincidence between Hα emitting filaments with 0.6 keV X-ray emitting gas is ∼ similar to that observed in the centers of cool core clusters undergoing AGN feedback. It is therefore possible that similar physical processes drive the evolution of these distinct filament regions, at least after an initial disturbance. As discussed in (e.g. Fabian et al., 2008b, 2011; Werner et al., 2012), a potential energy source powering the FIR through UV emission (including the Hα+N II optical line emission) in cooling core clusters are hot ICM particles penetrating into the filaments of cold gas,

134 7.3. THE NATURE OF RAM PRESSURE STRIPPING IN CLUSTER GALAXIES IN THE VICINITY OF M86

resulting in collisional ionization and heating (see Ferland et al., 2008, 2009). Werner et al. (2012) propose that shear instabilities due to shocks or the motion of the filaments through the ambient ICM may facilitate mixing between the hot and cold gas phases. The physics of the multiphase gas in the M86 filament region, which is exposed to shear instabilities as it moves through the Virgo ICM and merger shocks due to the infall of the M86 group, may therefore share similarities with the physics of the optical emission line nebulae seen in the cooling cores of galaxy clusters. One key difference between the filament region discussed here with those observed near cool core clusters, however, is the nature of the initial disturbance. Rather than instabilities triggered by AGN feedback, the collision between M86 and NGC 4438 would be the source of the shearing here. Such triggering may be further encouraged by M86’s supersonic motion and subsequent shock heating. Sites within 10 kpc of the two galaxies have recently been shown to host CO(1 0) line ∼ → emission, suggesting that molecular hydrogen gas is present between the two galaxies for time scales of at least 100 Myr (Dasyra et al., 2012). This molecular gas is inferred to originate ∼ primarily from NGC 4438, similar to what we infer for the X-ray emission. Further observations that place stricter constraints on the star formation rate and detailed spectral properties of this region, as well as the mass of cold gas present from emission lines such as C II, will be required to test the connection between the Hα emission presented here and that observed in cool core clusters.

7.3.3 Combining AGN Feedback with Ram Pressure Stripping

The detection of a clear tail of X-ray emission trailing to the south of M84 offers another example of a galaxy where feedback and ram pressure stripping work in conjunction to strip its X-ray halo. Ram pressure stripping combined with AGN feedback are also observed in Sersic 159-03 (Werner et al., 2011) and the Virgo elliptical galaxy NGC 4552 (Machacek et al., 2006a,b), and similar processes are observed at larger scales in the Ophiuchus Cluster (Million et al., 2010b) and MACSJ1931.8- 2634 (Ehlert et al., 2011). In these systems, the combined influences of these two processes can rapidly suppress or even destroy cool cores associated with the central regions of the galaxy/galaxy cluster. It appears as though a configuration where both AGN feedback and ram pressure stripping occur simultaneously is more common than initially expected. The energy injected into the X-ray halo from AGN feedback may allow M84 to be stripped more 1 efficiently now than in its past. At its current stripping rate of . 7M yr− estimated above, all of ⊙ the X-ray gas should be stripped from this galaxy within 200 300 Myr, a time scale likely far ∼ − shorter than the time elapsed since M84 first crossed the Virgo Cluster’s virial radius. We determine

135 CHAPTER 7. THE PHYSICAL PICTURE

whether losses from stellar winds may be sufﬁcient to replenish the X-ray halo of M84, assuming a single-age passively evolving stellar population with a Salpeter IMF. We use the stellar mass-loss rate from Ciotti et al. (1991) to estimate this rate, M⋆(t), as

11 1.3 M⋆(t) 1.5 10− LBt− (7.1) ≃ × 15 10 where LB is the present-day B-band luminosity in units of LB, (LB = 4.5 10 LB, for M84; ⊙ × ⊙ 2 Finoguenov & Jones, 2002) and t15 is the age of the stellar population in units of 15 Gyr . The local stellar mass-loss rate, assuming that the current stellar population is 10 Gyr old, is of the same 1 order-of-magnitude as the rate of ram pressure stripping (M⋆ 1M yr− ). ∼ ⊙

7.3.4 On the Frequency of Stripping in Cluster Member Galaxies

Perhaps surprisingly, each of the four X-ray emitting galaxies detected in this mosaic show evidence of being stripped as they traverse through the ICM. Although these galaxies share some common features, no two galaxies are observed to have stripped gas tails sharing all of the same properties. The prevalence of gas trailing in the wake of galaxies in this busy region of the Virgo Cluster suggests that stripping processes may play an important role for nearly all galaxies in clusters. The particular observational signatures associated with the stripping process may depend strongly on the morphology, mass, and gas content of the host galaxy, but it appears typical that gas initially hosted by galaxies is efficiently stripped by the ICM. For more massive elliptical galaxies hosting large masses of X-ray emitting gas like M86 (likely the central galaxy of an infalling group) and M84, the stripping is most readily observed as tails of X-ray emitting gas trailing the galaxies. The stripped X-ray gas behind these two galaxies, however, suggest that the astrophysical processes that drive the stripping may vary between different sites. One key difference between the stripped X-ray gas between M86 and M84 is the relative length of the apparent tails, which may indicate differences in the gas physics between the two sites. The tail behind M86 remains roughly uniform in width and coherent for distances of at least 150 kpc ∼ in projection without significant evidence for mixing with the surrounding ICM. The true length of this tail is also likely significantly larger than its observed length in projection due to M86’s high velocity with respect to Virgo ( 1500 km s 1) along the line-of-sight. Maintaining such cohesion ∼ − over these distances suggests that the stripped gas is undergoing a laminar flow even as the galaxy traverses through the ICM supersonically, and subsequently suggests that the stripped gas may have

2This formula is valid in the range from 0.5 to over 15 Gyr.

136 7.4. THE INFLUENCES OF THE ICM ON AGN

high viscosity. The gas trailing behind M84, on the other hand, is only observable for distances of 15 kpc in surface brightness and temperature maps, and the lower velocity of M84 with respect ∼ to the Virgo ICM along the line-of-sight ( 200 300 km s 1) indicates that this tail is probably not ∼ − − significantly longer than its projected length. The temperature structure in this tail is also observed to increase smoothly with distance from M84. These may be evidence for more efficient mixing between the stripped gas and the surrounding ICM at this site, perhaps associated with a more turbulent flow of the stripped gas. Deeper observations will be required to determine the full length of the stripped gas trailing M84 and subsequently the extent to which turbulence and magnetic fields may be necessary to account for the thermodynamic structure in M84’s tail. Spiral galaxies contain significant masses of gas at much lower temperatures ( 10 100 K). ∼ − The observed differences in the stripped cold gas behind NGC 4388 and NGC 4438 suggest that the manner in which these phases interact and intersperse with the ICM may vary also significantly from site to site, and that the presence of shear instabilities or shocks may play an important role in these processes. This also appears to be true for the X-ray halos, especially when the disruptions due to the surrounding environment are more extreme. Deeper multiwavelength observations will again be necessary to fully understand the range of gas temperatures present in these stripped tails and test the stripping processes from start to finish.

7.4 The Inﬂuences of the ICM on AGN

Using the point source catalogs from one of the largest X-ray point source surveys every undertaken, we have been able to probe the X-ray AGN population in galaxy clusters at an unprecedented level of precision and accuracy. The X-ray catalogs for 43 clusters in this sample are supplemented by deep optical imaging, which allows for even more powerful tests of the AGN population in galaxy clusters. We discuss the physical implications in the sections below.

7.4.1 Triggering AGN by Harassment

Our results are in tension with studies that have claimed higher rates of AGN near the viral radii of clusters than the ﬁeld (e.g. Ruderman & Ebeling, 2005). While starbursts have been observed in a few instances in the outskirts of galaxy clusters (e.g. Moran et al., 2005), we ﬁnd no evidence in our data that the cluster outskirts provide sites of ubiquitous X-ray AGN activity. Processes and models that predict higher rates of starbursts and subsequent AGN activity in galaxy clusters with respect

137 CHAPTER 7. THE PHYSICAL PICTURE

to the ﬁeld are therefore disfavored by our data.

7.4.2 The Fraction of Galaxies Hosting X-ray AGN

Our results provide the best measurements to date of how the fraction of galaxies hosting X-ray AGN varies throughout the cluster environment. It is clear from our data that the fraction of galaxies hosting X-ray AGN in clusters is consistently lower than the field: the suppression is mild near the edges of the clusters but increases by a factor of 3 4 within r500. Our result places new ∼ − ∼ constraints on the physical processes driving the evolution of AGN in galaxy clusters. Detection of such an excess has been claimed in some previous studies (Ruderman & Ebeling, 2005; Fassbender, Suhadaˇ & Nastasi, 2012) utilizing smaller samples of galaxy clusters. Our analysis reveals a source density that smoothly and monotonically decreases with radius until it is indistinguishable from the field population. Although galaxy-galaxy mergers are a commonly proposed mechanism for triggering X-ray AGN in the field (e.g. Cappelluti, Allevato & Finoguenov, 2012), and such mergers are the proposed origin for observed central starburst galaxies (e.g. Moore, Lake & Katz, 1998; Moran et al., 2005) near the virial radii of clusters, it appears that these regions do not trigger AGN at a higher rate than in the field. The sample of Fassbender, Suhadaˇ & Nastasi (2012) uses higher redshift galaxy clusters than this sample, however, so it is possible that harassment triggered AGN may be more frequent at higher redshifts than are considered here. Ram pressure stripping of galaxy gas during infall may contribute to the observed suppression of AGN activity in clusters. However, our sample size is not large enough to robustly determine the gradient of the cluster-specific AGN fraction profile and subsequently the time scale over which ram pressure stripping shuts off AGN activity in cluster galaxies. Similar studies investigating star formation in cluster galaxies (e.g. von der Linden et al., 2010; Wetzel, Tinker & Conroy, 2012) and optical AGN in clusters (e.g. von der Linden et al., 2010; Pimbblet et al., 2013), however, disfavor scenarios where more efficient versions of ram pressure stripping are operating. We emphasize that these studies show optical AGN activity being suppressed to a similar extent as the X-ray AGN results presented here, roughly a factor of 3 4 between the cluster centers and outskirts. The fact ∼ − that X-ray AGN in cluster galaxies are suppressed to a similar degree as star formation and optical AGN activity provides further evidence for a connection between the three processes. Although our results are consistent with X-ray AGN in cluster galaxies being slowly suppressed in a similar manner to optical AGN and star formation, key questions still remain as to how ram pressure stripping transforms galaxies in clusters. This slow suppression has been proposed to

138 7.4. THE INFLUENCES OF THE ICM ON AGN

originate in the stripping of hot, diffuse halo gas from cluster galaxies, effectively starving them of gas reservoirs to replenish the cold central gas after it is processed by star formation. It is clear from X-ray observations of cluster galaxies, however, that this simple model where hot halo gas is rapidly ( 10 100 Myr) and permanently stripped from cluster galaxies during infall while cold gas ∼ − remains bound is inconsistent with observations of ubiquitous, ongoing stripping of hot gas and the common presence of extended X-ray halos for massive galaxies in nearby clusters. If hot halo gas is stripped on short time scales when compared to the galaxy crossing time, it must be replenished at comparable rates. A more complex interplay between the ICM, hot halo gas, and cold central gas in cluster galaxies is necessary to account for the whole range of observations. The higher fraction of X-ray AGN observed in BCGs is consistent with previous work (e.g. Hlavacek-Larrondo et al., 2013, and references therein) and demonstrates that BCGs may be subject to unique processes that trigger AGN with higher efﬁciency than typical cluster galaxies. This may be related to the presence of cool cores in galaxy clusters (Best et al., 2007; von der Linden et al., 2007; Fabian, 2012); we note that both of the BCGs hosting X-ray AGN in this sample are in cool core clusters. However, not all BCGs in cool cores (even those undergoing clear radio-mode AGN feedback) host bright X-ray AGN. A second possible triggering mechanism for BCGs is the uniquely high rates of tidal interactions and mergers that occur between the BCG and other orbiting galaxies. A recent merger between an orbiting galaxy and the BCG may supply the cold gas to fuel the X-ray AGN. With only one or two X-ray bright AGN in this sample, however, no robust tests can be performed that can distinguish between these two scenarios.

7.4.3 Evolution Beyond Self-Similarity

It is also clear from these results that AGN in clusters do not evolve self-similarly. Instead, the 1 overdensity of AGN with respect to the ﬁeld appears to scale as M500− , with massive clusters hosting galaxy clusters at lower rates than less massive clusters. It is not clear what the astrophysical origin of this signal may be, although we emphasize that there is no evidence for evolution beyond self- similarity in the galaxy clusters themselves (e.g. Mantz et al., 2010a; Maughan et al., 2012). Using 1 virial arguments, the M− scaling we ﬁnd in the overall overdensity implies that the rate of triggering AGN in clusters would scale with the velocity dispersion σ as σ 3. This is the same scaling rate ∼ − as predicted for the merger rate of galaxies in clusters (Mamon, 1992), and may provide a new line of evidence that mergers are the dominant mechanism by which AGN are triggered. Other physical mechanisms may result in the same scaling relation ( M 1 or σ 3), but we emphasize that ∼ − ∼ − 139 CHAPTER 7. THE PHYSICAL PICTURE

any proposed physical explanation must maintain the same spatial distribution of cluster member AGN across all masses and redshifts. Additional studies into the multiwavelength properties of the galaxies hosting these X-ray AGN will be necessary to determine whether or not they originate in mergers or another process with the same predicted scaling relation. Taking our interpretation at face value, however, makes it clear how important galaxy mergers are to triggering even moderate luminosity AGN. Although AGN at high luminosities and redshifts 44 1 (L & 10 erg s− , z & 1) are likely triggered by mergers (Hopkins & Beacom, 2006; Hopkins et al., 2008; Hasinger, 2008), the cluster AGN discussed here are certainly not at those redshifts and the majority of them are not quite so luminous. The fact that their overdensity appears to scale in the same manner as the merger rate offers compelling evidence for a connection between galaxy mergers and AGN activity, even in the moderate luminosity range. Such a model does suggest that AGN in clusters are suppressed both passively (by the lower overall rates of galaxy mergers) and actively (by ram pressure stripping of galaxy gas during infall). This scaling relation may play an important role in understanding the apparently sudden evolution in the AGN fraction in galaxy clusters over cosmic time. We have already demonstrated that the AGN fraction in these galaxy clusters is 3 4 times lower than the ﬁeld, while studies of ∼ − higher redshift clusters and groups (z 1) appear to host higher fractions of AGN than in the ﬁeld ∼ (Lehmer et al., 2013; Martini et al., 2013). The origin of the turnaround between these higher redshift clusters and lower redshift systems may in fact be driven by the fact that galaxy clusters grow continuously more massive over cosmic time, and not explicitly depend on the cluster redshift.

140 Chapter 8

Unsolved Questions and Near Term Prospects for Galaxies in Clusters

8.1 Introduction

While the work presented here represents the most complete study of X-ray selected AGN in galaxy clusters to date, many questions regarding the evolution and fueling of AGN in galaxy clusters nevertheless remain unanswered. These questions, along with the prospects for near term studies to address these questions, are discussed below. All of these projects are currently underway, although they are at different stages of completion.

8.2 Statistical Background Subtraction of Field Galaxies

While the spatial distribution of AGN in galaxy clusters have already been measured, here and elsewhere, in flux limited samples of galaxies and AGN, a better physical understanding of the evolution of AGN in clusters requires the use of samples that are limited by physically motivated quantities instead of detector motivated quantities. As stated above, neither the X-ray nor optical imaging data have the ability to specifically identify which galaxies and AGN are cluster members, and the foreground/background galaxies are a major source of contamination in the desired cluster galaxy signal. In order to account for the background galaxy population in these fields, a statistical subtraction algorithm can be employed, using expectations of the field galaxy population surveys such as COSMOS. We here present such an algorithm. In order to identify the cluster member galaxy and AGN populations statistically, we will place all of the galaxies detected by COSMOS on a color-magnitude diagram, using the V R color and − 141 CHAPTER 8. FUTURE STUDIES

R-band magnitude1. We will then plot the color-magnitude diagrams for the cluster fields in the same filter set. Binning up the color-magnitude diagrams for both the cluster fields and COSMOS in bins of 1 1 in color-magnitude space, and determine the number of galaxies in both the cluster × (Ncluster+field) and COSMOS fields (NCOSMOS). With these counts, we will determine the probability of any particular galaxy in the cluster fields being a field galaxy as

A NCOSMOS Pfield = × (8.1) Ncluster+field where A is a normalization factor that takes the different survey areas of the two fields into account. For each galaxy in the clusters, assign a random number between 0 and 1 will be assigned and only keep those sources where this random number is larger than the Pfield for that region in color-magnitude space. This process will be done independently in several radial bins in order to ensure that the spatial distribution of the cluster member galaxies is not significantly altered by our procedure.

For regions where there are insufﬁcient cluster galaxies to robustly subtract the ﬁeld population

(i.e. Pﬁeld > 1, the “negative galaxy” problem), we will expand the region of color magnitude space we inspect to a 2 2 bin in color-magnitude space, continuing to larger and larger spaces as needed. × This procedure is not a unique solution to the “negative galaxy” problem. This particular choice, however, has the distinct advantage of maintaining the original probability distribution across the entire color-magnitude space, albeit smeared out in regions where few excess cluster galaxies are detected.

When complete, a ﬁeld subtracted population of cluster member galaxies and X-ray AGN will enable several new key tests to be performed. The position of the host galaxies for cluster member X-ray AGN on the color-magnitude diagram, for example, will allow us to determine the extent to which AGN in clusters are hosted in galaxies on the red sequence. Such a calculation will also allow for the fraction of galaxies hosting X-ray AGN to be calculated using samples limited by absolute magnitude or stellar mass.

1For clusters that are not observed with the V and R band ﬁlters with Subaru, we interpolate these magnitudes using the observed g and r band ﬁlter magnitudes from the CFHT telescope.

142 8.3. SPECTROSCOPICALLY CONFIRMED CLUSTER MEMBER AGN

8.3 Spectroscopically Conﬁrmed Cluster Member AGN

With a sufficiently large sample of spectroscopically identified cluster member AGN, a number of new tests that link the ICM to the emission and accretion of gas in cluster galaxies become available. However, acquiring optical spectra for all of the galaxy clusters in our sample is expensive and time consuming, so the majority of the clusters in this sample have only minimal spectroscopic data. 2 However, using a combination of public databases and newly proposed observations, we are able establish a clear path forward in testing the properties of X-ray AGN in galaxy clusters with respect to both similar cluster member galaxies and similar X-ray AGN in the field. We discuss the early stages of these two tests in the sections below.

8.3.1 X-ray Spectra of Cluster Member AGN

Although it is clear from this work that the ICM typically quenches AGN activity in galaxy clusters, it remains to be tested as to whether or not the ICM influences the small scale accretion process itself. For such a study, it is necessary to determine the typical X-ray emission spectra of cluster member AGN and compare them to what is expected from AGN observed in the CDFS or COS- MOS. We identify cluster member AGN by using publicly available redshift data in the NASA/IPAC Extragalactic Database (NED). We performed automated search queries within a 2 arcsec radius of each X-ray source position, in order to determine whether or not it has a counterpart in NED with a measured redshift. If the X-ray source had a counterpart with a measured redshift within 5% of the cluster redshift, it was determined to be a cluster member AGN. In total, 78 sources were determine to be spectroscopically confirmed cluster member AGN by this method. Stacking the spectra of all of these sources is currently underway, and will offer new insights as to how the ICM may influence the small scale accretion processes at work in cluster member AGN.

8.3.2 The Host Galaxies of Cluster Member AGN

While the tests described above allow for a robust comparison between X-ray AGN in clusters and X-ray AGN in the ﬁeld, we are unable to draw any conclusions as to the nature of the galaxies hosting these AGN from the X-ray data alone. In order to more fully understand the properties of cluster galaxies that host X-ray AGN, deep multi-object optical spectroscopy of galaxies in clusters

2All of the galaxy clusters in this sample at least have spectra for their BCG’s, in order to determine the overall cluster redshift. The availability of galaxy spectra in addition to the BCG, however, varies signiﬁcantly between the different clusters in this sample.

143 CHAPTER 8. FUTURE STUDIES

is necessary. In conjunction with this X-ray AGN project, our group has recently been awarded a large programme with the VIMOS multi-object spectrograph on the Very Large Telescope () on 10 of the galaxy clusters discussed in this thesis (Programme 090.A-0958, P.I. Anja von der Linden). With these observations, we will acquire 400 optical spectra per galaxy cluster, including nearly ∼ all of the X-ray point sources in these ﬁelds of view. Optical spectra of the X-ray point sources will not only allow for the opportunity to measure source redshifts and establish cluster membership, but also allow for measurements of the star formation and dust content of the host galaxies for cluster AGN. These properties will be compared statistically to the population of similarly massive cluster galaxies in order to test whether AGN in clusters are unique with respect to normal cluster galaxies in some observable way.

8.4 Correlating X-ray and Radio AGN

This work focuses primarily on X-ray AGN, which are typically inferred to be fueled by the accretion of cold gas, sufficiently cold to form stars. However, as discussed in the Introduction, the consensus view is that radio-loud AGN are commonly fueled by the accretion of hot, X-ray emitting gas (or gas cooling from these temperatures). Radio loud AGN are measured to be more frequent in clusters than in the field, and especially frequent in BCG’s (e.g. Best, 2004; Best et al., 2007; Best & Heckman, 2012). Combining the X-ray catalogs produced in this study with the publicly available FIRST radio survey at 1.4 GHz (Becker, White & Helfand, 1995) and new radio observations, we will be able to cross-correlate X-ray AGN with radio sources, and determine the fraction of cluster X-ray sources hosting radio emission may vary across the cluster environment and compare it to expectations in the field.

8.5 Larger Samples at Higher Redshift and Lower Mass

It is clear from the studies discussed in this thesis that while the sample of X-ray AGN discussed in this thesis is the largest ever assembled for this sort of work (and one of the largest X-ray AGN surveys ever conducted), we are ultimately limited at nearly every step of our analysis by the statistical ﬂuctuations on the X-ray source counts. The statistics are particularly limited for our tests of the AGN fraction using 43 galaxy clusters as well as determinations of how the AGN population in galaxy clusters evolves with redshift.

144 8.5. LARGER SAMPLES AT HIGHER REDSHIFT AND LOWER MASS

Larger samples of galaxy clusters will be required to improve the statistical precision and physical interpretation of these results. Substantial improvements in the AGN fraction curve (in Chapter 6) will require at least a factor of 4 larger samples of galaxy clusters with deep X-ray and opti- ∼ cal imaging data, and even larger samples will be required to perform similar analyses with stellar mass or luminosity limited samples of galaxies and AGN. In order to provide additional constraining power on the redshift evolution of AGN in galaxy clusters, considerably larger samples of high (0.5 < z < 1.0) redshift galaxy clusters are necessary. It is fortunate, however, that the analysis procedure for producing X-ray point source catalogs and sensitivity maps is reasonably well automated, and can straightforwardly be applied to nearly all Chandra observations of galaxy clusters. All that is required from independent observations is a measurement of the cluster redshift and mass. New surveys of galaxy clusters selected by their SZ signals such as the South Pole Telescope (SPT), the Atacama Cosmology Telescope (ACT), and the Planck satellite have already detected dozens of new galaxy clusters. Just as importantly, SZ surveys are far more efficient at detecting massive galaxy clusters at high redshift than X-ray or optical surveys, since the measured SZ decre- ment is independent of redshift. The majority of these clusters have recently been (and are still being) followed up with Chandra X-ray observations over the past few years, providing a much larger sample of high redshift galaxy clusters than ever before. The methods and procedures discussed here can be applied to any galaxy cluster observation with Chandra. Once the Chandra data for these clusters are made available, these new clusters will allow not only for more precise measurements at higher spatial resolution to be performed, but will also provide new leverage for the tests already discussed in this thesis, in particular regarding the evolution of AGN in clusters over cosmic time. New wide field optical imaging surveys such as the Dark Energy Survey (DES) and Large Synoptic Survey Telescope (LSST) will provide deep optical coverage over large areas of the sky. Therefore the ability to acquire pointed X-ray observations of galaxy clusters in these optical survey fields with Chandra will ultimately limit the statistical precision with which similar studies calculating the X-ray AGN fraction can be performed. Chandra is currently the only available X-ray instrument with the spatial resolution and sensitivity to perform such tests, and will remain so for the foreseeable future. It is therefore critical that it continue to observe large numbers of galaxy clusters and the point sources in those fields so that we can continue to test our understanding of the evolution of galaxies in clusters to new levels of precision.

145 CHAPTER 8. FUTURE STUDIES

146 Bibliography

Abell G. O., 1965, ARA&A, 3, 1

Alexander D. M. et al., 2003, AJ, 126, 539

Alexander D. M., Brandt W. N., Hornschemeier A. E., Garmire G. P., Schneider D. P., Bauer F. E., Grifﬁths R. E., 2001, AJ, 122, 2156

Allen S. W., 1995, MNRAS, 276, 947

Allen S. W., Dunn R. J. H., Fabian A. C., Taylor G. B., Reynolds C. S., 2006, MNRAS, 372, 21

Allen S. W., Evrard A. E., Mantz A. B., 2011, ARA&A, 49, 409

Allen S. W., Fabian A. C., 1998, MNRAS, 297, L63

Allen S. W., Fabian A. C., Johnstone R. M., Arnaud K. A., Nulsen P. E. J., 2001, MNRAS, 322, 589

Allen S. W., Rapetti D. A., Schmidt R. W., Ebeling H., Morris R. G., Fabian A. C., 2008, MNRAS, 383, 879

Allen S. W., Schmidt R. W., Ebeling H., Fabian A. C., van Speybroeck L., 2004, MNRAS, 353, 457

Allen S. W., Schmidt R. W., Fabian A. C., 2002, MNRAS, 334, L11

Anders E., Grevesse N., 1989, Geochim. Cosmochim. Acta, 53, 197

Applegate D. E. et al., 2012, ArXiv e-prints:#1208.0605

Arnaud K., 2004, in Bulletin of the American Astronomical Society, Vol. 36, Bulletin of the Amer- ican Astronomical Society, pp. 934–+

Ascasibar Y., Markevitch M., 2006, ApJ, 650, 102

Balmaverde B., Baldi R. D., Capetti A., 2008, A&A, 486, 119

147 BIBLIOGRAPHY

Balucinska-Church M., McCammon D., 1992, ApJ, 400, 699

Bauer F. E., Alexander D. M., Brandt W. N., Schneider D. P., Treister E., Hornschemeier A. E., Garmire G. P., 2004, AJ, 128, 2048

Bauer F. E., Fabian A. C., Sanders J. S., Allen S. W., Johnstone R. M., 2005, MNRAS, 359, 1481

Baum S. A., O’Dea C. P., 1991, MNRAS, 250, 737

Becker R. H., White R. L., Helfand D. J., 1995, ApJ, 450, 559

Behroozi P. S., Wechsler R. H., Conroy C., 2013, ApJ, 770, 57

Bekki K., Couch W. J., Shioya Y., 2002, ApJ, 577, 651

Best P. N., 2004, MNRAS, 351, 70

Best P. N., Heckman T. M., 2012, MNRAS, 421, 1569

Best P. N., von der Linden A., Kauffmann G., Heckman T. M., Kaiser C. R., 2007, MNRAS, 379, 894

Binggeli B., Popescu C. C., Tammann G. A., 1993, A&AS, 98, 275

Binggeli B., Tammann G. A., Sandage A., 1987, AJ, 94, 251

Bˆırzan L., McNamara B. R., Nulsen P. E. J., Carilli C. L., Wise M. W., 2008, ApJ, 686, 859

Bˆırzan L., Rafferty D. A., McNamara B. R., Wise M. W., Nulsen P. E. J., 2004, ApJ, 607, 800

Bohringer¨ H., Briel U. G., Schwarz R. A., Voges W., Hartner G., Trumper¨ J., 1994, Nat, 368, 828

Bohringer¨ H. et al., 2004, A&A, 425, 367

Bohringer¨ H., Werner N., 2010, A&AR, 18, 127

Boselli A. et al., 2011, A&A, 528, A107

Brandt W. N., Alexander D. M., 2010, Proceedings of the National Academy of Science, 107, 7184

Brandt W. N. et al., 2001, AJ, 122, 2810

148 BIBLIOGRAPHY

Brandt W. N., Hasinger G., 2005, ARA&A, 43, 827

Broos P. S., Townsley L. K., Feigelson E. D., Getman K. V., Bauer F. E., Garmire G. P., 2010, ApJ, 714, 1582

Bruggen¨ M., Kaiser C. R., 2002, Nat, 418, 301

Brusa M. et al., 2010, ApJ, 716, 348

Brusa M. et al., 2007, ApJS, 172, 353

Budzynski J. M., Koposov S., McCarthy I. G., McGee S. L., Belokurov V., 2012, ArXiv e-prints

Bullock J. S., Kolatt T. S., Sigad Y., Somerville R. S., Kravtsov A. V., Klypin A. A., Primack J. R., Dekel A., 2001, MNRAS, 321, 559

Buote D. A., 2002, ApJL, 574, L135

Burenin R. A., Vikhlinin A., Hornstrup A., Ebeling H., Quintana H., Mescheryakov A., 2007, ApJS, 172, 561

Burns J. O., Hallman E. J., Gantner B., Motl P. M., Norman M. L., 2008, ApJ, 675, 1125

Canning R. E. A., Fabian A. C., Johnstone R. M., Sanders J. S., Conselice C. J., Crawford C. S., Gallagher, III J. S., Zweibel E., 2010, ArXiv e-prints

Capak P. et al., 2007, ApJS, 172, 99

Cappellari M., Copin Y., 2003, MNRAS, 342, 345

Cappelluti N., Allevato V., Finoguenov A., 2012, Advances in Astronomy, 2012

Cappelluti N. et al., 2009, A&A, 497, 635

Cappelluti N., Cappi M., Dadina M., Malaguti G., Branchesi M., D’Elia V., Palumbo G. G. C., 2005, A&A, 430, 39

Cappelluti N. et al., 2007, ApJS, 172, 341

Cappi M. et al., 2001, ApJ, 548, 624

149 BIBLIOGRAPHY

Carlberg R. G. et al., 1997, ApJL, 485, L13

Carter J. A., Read A. M., 2007, A&A, 464, 1155

Cash W., 1979, ApJ, 228, 939

Cavagnolo K. W., Donahue M., Voit G. M., Sun M., 2009, ApJS, 182, 12

Cavagnolo K. W., McNamara B. R., Nulsen P. E. J., Carilli C. L., Jones C., Birzan L., 2010, ArXiv e-prints

Churazov E., Forman W., Jones C., Bohringer¨ H., 2003, ApJ, 590, 225

Churazov E., Sunyaev R., Forman W., Bohringer¨ H., 2002, MNRAS, 332, 729

Ciotti L., D’Ercole A., Pellegrini S., Renzini A., 1991, ApJ, 376, 380

Conselice C. J., Gallagher, III J. S., Wyse R. F. G., 2001, AJ, 122, 2281

Cowie L. L., Binney J., 1977, ApJ, 215, 723

Cowie L. L., Garmire G. P., Bautz M. W., Barger A. J., Brandt W. N., Hornschemeier A. E., 2002, ApJL, 566, L5

Crawford C. S., Allen S. W., Ebeling H., Edge A. C., Fabian A. C., 1999, MNRAS, 306, 857

Crawford C. S., Sanders J. S., Fabian A. C., 2005, MNRAS, 361, 17

Croom S. M., Smith R. J., Boyle B. J., Shanks T., Miller L., Outram P. J., Loaring N. S., 2004, MNRAS, 349, 1397

Croton D. J. et al., 2006, MNRAS, 365, 11

Crowl H. H., Kenney J. D. P., van Gorkom J. H., Vollmer B., 2005, AJ, 130, 65

Dasyra K. M., Combes F., Salome´ P., Braine J., 2012, A&A, 540, A112

De Grandi S., Molendi S., 2001, ApJ, 551, 153

De Luca A., Molendi S., 2004, A&A, 419, 837 de Plaa J., Werner N., Bleeker J. A. M., Vink J., Kaastra J. S., Mendez´ M., 2007, A&A, 465, 345

150 BIBLIOGRAPHY

Diehl S., Statler T. S., 2006, MNRAS, 368, 497

Dressler A., 1980, ApJ, 236, 351

Duffy A. R., Schaye J., Kay S. T., Dalla Vecchia C., 2008, MNRAS, 390, L64

Dunn R. J. H., Allen S. W., Taylor G. B., Shurkin K. F., Gentile G., Fabian A. C., Reynolds C. S., 2010, MNRAS, 404, 180

Dunn R. J. H., Fabian A. C., 2004, MNRAS, 355, 862

Dunn R. J. H., Fabian A. C., 2008, MNRAS, 385, 757

Dunn R. J. H., Fabian A. C., Taylor G. B., 2005, MNRAS, 364, 1343

Dupke R., White, III R. E., Bregman J. N., 2007, ApJ, 671, 181

Dupke R. A., White, III R. E., 2000, ApJ, 528, 139

Ebeling H., Barrett E., Donovan D., Ma C.-J., Edge A. C., van Speybroeck L., 2007, ApJL, 661, L33

Ebeling H., Edge A. C., Bohringer H., Allen S. W., Crawford C. S., Fabian A. C., Voges W., Huchra J. P., 1998, MNRAS, 301, 881

Ebeling H., Edge A. C., Mantz A., Barrett E., Henry J. P., Ma C. J., van Speybroeck L., 2010, MNRAS, 407, 83

Ehlert S. et al., 2011, MNRAS, 411, 1641

Ehlert S., Ulmer M. P., 2009, A&A, 503, 35

Ehlert S., Werner N., Simionescu A., Allen S. W., Kenney J. D. P., Million E. T., Finoguenov A., 2013, MNRAS, 430, 2401

Elvis M. et al., 2009, ApJS, 184, 158

Erben T. et al., 2005, Astronomische Nachrichten, 326, 432

Fabbiano G., 1989, ARA&A, 27, 87

151 BIBLIOGRAPHY

Fabian A. C., 2012, ArXiv e-prints

Fabian A. C., Allen S. W., Crawford C. S., Johnstone R. M., Morris R. G., Sanders J. S., Schmidt R. W., 2002, MNRAS, 332, L50

Fabian A. C., Johnstone R. M., Sanders J. S., Conselice C. J., Crawford C. S., Gallagher, III J. S., Zweibel E., 2008a, Nat, 454, 968

Fabian A. C., Johnstone R. M., Sanders J. S., Conselice C. J., Crawford C. S., Gallagher, III J. S., Zweibel E., 2008b, Nat, 454, 968

Fabian A. C., Nulsen P. E. J., 1977, MNRAS, 180, 479

Fabian A. C., Sanders J. S., Allen S. W., Crawford C. S., Iwasawa K., Johnstone R. M., Schmidt R. W., Taylor G. B., 2003, MNRAS, 344, L43

Fabian A. C., Sanders J. S., Taylor G. B., Allen S. W., Crawford C. S., Johnstone R. M., Iwasawa K., 2006, MNRAS, 366, 417

Fabian A. C., Sanders J. S., Williams R. J. R., Lazarian A., Ferland G. J., Johnstone R. M., 2011, MNRAS, 417, 172

Fabian A. C., Schwarz J., Forman W., 1980, MNRAS, 192, 135

Fassbender R., Suhadaˇ R., Nastasi A., 2012, Advances in Astronomy, 2012

Ferland G. J., Fabian A. C., Hatch N. A., Johnstone R. M., Porter R. L., van Hoof P. A. M., Williams R. J. R., 2008, MNRAS, 386, L72

Ferland G. J., Fabian A. C., Hatch N. A., Johnstone R. M., Porter R. L., van Hoof P. A. M., Williams R. J. R., 2009, MNRAS, 392, 1475

Finoguenov A., Jones C., 2002, ApJ, 574, 754

Finoguenov A., Pietsch W., Aschenbach B., Miniati F., 2004, A&A, 415, 415

Finoguenov A., Ruszkowski M., Jones C., Bruggen¨ M., Vikhlinin A., Mandel E., 2008, ApJ, 686, 911

Forman W. et al., 2007, ApJ, 665, 1057

152 BIBLIOGRAPHY

Forman W. et al., 2005, ApJ, 635, 894

Forman W., Schwarz J., Jones C., Liller W., Fabian A. C., 1979, ApJL, 234, L27

Freeman P. E., Kashyap V., Rosner R., Lamb D. Q., 2002, ApJS, 138, 185

Galametz A. et al., 2009, ApJ, 694, 1309

Gao L., Navarro J. F., Cole S., Frenk C. S., White S. D. M., Springel V., Jenkins A., Neto A. F., 2008, MNRAS, 387, 536

Gebhardt K. et al., 2000, ApJL, 539, L13

Gehrels N., 1986, ApJ, 303, 336

Georgakakis A. et al., 2009, MNRAS, 397, 623

Giacconi R. et al., 2002, ApJS, 139, 369

Gilmour R., Best P., Almaini O., 2009, MNRAS, 392, 1509

Gonzalez A. H., Zabludoff A. I., Zaritsky D., 2005, ApJ, 618, 195

Goto T., Yagi M., Tanaka M., Okamura S., 2004, MNRAS, 348, 515

Goto T., Yamauchi C., Fujita Y., Okamura S., Sekiguchi M., Smail I., Bernardi M., Gomez P. L., 2003, MNRAS, 346, 601

Grevesse N., Sauval A. J., 1998, Space Sci. Rev., 85, 161

Gunn J. E., Gott, III J. R., 1972, ApJ, 176, 1

Guo F., Mathews W. G., 2010, ArXiv e-prints

Haardt F., Maraschi L., 1991, ApJL, 380, L51

Haggard D., Green P. J., Anderson S. F., Constantin A., Aldcroft T. L., Kim D.-W., Barkhouse W. A., 2010, ApJ, 723, 1447

Hardcastle M. J., Evans D. A., Croston J. H., 2007, MNRAS, 376, 1849

Hasinger G., 2008, A&A, 490, 905

153 BIBLIOGRAPHY

Hasinger G. et al., 2007, ApJS, 172, 29

Hasinger G., Miyaji T., Schmidt M., 2005, A&A, 441, 417

Hasler N. et al., 2012, ApJ, 748, 113

Hatch N. A., Crawford C. S., Fabian A. C., 2007, MNRAS, 380, 33

Heckman T. M., Ptak A., Hornschemeier A., Kauffmann G., 2005, ApJ, 634, 161

Henry J. P., Finoguenov A., Briel U. G., 2004, ApJ, 615, 181

Hlavacek-Larrondo J., Fabian A. C., Edge A. C., Ebeling H., Allen S. W., Sanders J. S., Taylor G. B., 2013, MNRAS, 431, 1638

Hlavacek-Larrondo J., Fabian A. C., Edge A. C., Ebeling H., Sanders J. S., Hogan M. T., Taylor G. B., 2012, MNRAS, 421, 1360

Hopkins A. M., 2004, ApJ, 615, 209

Hopkins A. M., Beacom J. F., 2006, ApJ, 651, 142

Hopkins P. F., Hernquist L., Cox T. J., Keresˇ D., 2008, ApJS, 175, 356

Hornschemeier A. E. et al., 2001, ApJ, 554, 742

Hubble E., Humason M. L., 1931, ApJ, 74, 43

Hudson D. S., Mittal R., Reiprich T. H., Nulsen P. E. J., Andernach H., Sarazin C. L., 2010, A&A, 513, A37

Johnson O., Best P. N., Almaini O., 2003, MNRAS, 343, 924

Johnstone R. M., Fabian A. C., Nulsen P. E. J., 1987, MNRAS, 224, 75

Kaastra J. S., Mewe R., 1993, A&AS, 97, 443

Kalberla P. M. W., Burton W. B., Hartmann D., Arnal E. M., Bajaja E., Morras R., Poppel¨ W. G. L., 2005, A&A, 440, 775

Kauffmann G. et al., 2003, MNRAS, 346, 1055

154 BIBLIOGRAPHY

Kawaguchi T., Shimura T., Mineshige S., 2001, ApJ, 546, 966

Kelly P. L. et al., 2012, ArXiv e-prints:#1208.0602

Kelson D. D., Zabludoff A. I., Williams K. A., Trager S. C., Mulchaey J. S., Bolte M., 2002, ApJ, 576, 720

Kenney J. D. P., Rubin V. C., Planesas P., Young J. S., 1995, ApJ, 438, 135

Kenney J. D. P., Tal T., Crowl H. H., Feldmeier J., Jacoby G. H., 2008, ApJL, 687, L69

Kennicutt, Jr. R. C., 1998a, ARA&A, 36, 189

Kennicutt, Jr. R. C., 1998b, ARA&A, 36, 189

Kim D.-W. et al., 2004, ApJS, 150, 19

King I., 1962, AJ, 67, 471

Koester B. P. et al., 2007, ApJ, 660, 221

Kovacˇ K. et al., 2010, ApJ, 708, 505

Kraft R. P. et al., 2011, ApJ, 727, 41

Kuntz K. D., Snowden S. L., 2000, ApJ, 543, 195

Lagana T. F., Andrade-Santos F., Lima Neto G. B., 2009, ArXiv e-prints

Landry D., Bonamente M., Giles P., Maughan B., Joy M., 2012, ArXiv e-prints

Larson R. B., Tinsley B. M., Caldwell C. N., 1980, ApJ, 237, 692

Leccardi A., Molendi S., 2008, A&A, 487, 461

Leccardi A., Rossetti M., Molendi S., 2010, A&A, 510, A82+

Lehmer B. D. et al., 2013, ApJ, 765, 87

Lehmer B. D. et al., 2012, ApJ, 752, 46

Liedahl D. A., Osterheld A. L., Goldstein W. H., 1995, ApJL, 438, L115

155 BIBLIOGRAPHY

Lin Y.-T., Mohr J. J., Stanford S. A., 2004, ApJ, 610, 745

Luo B. et al., 2008, ApJS, 179, 19

Luo B. et al., 2010, ApJS, 187, 560

Machacek M., Jones C., Forman W. R., Nulsen P., 2006a, ApJ, 644, 155

Machacek M., Nulsen P. E. J., Jones C., Forman W. R., 2006b, ApJ, 648, 947

Machacek M. E., Jones C., Forman W. R., 2004, ApJ, 610, 183

Magorrian J. et al., 1998, AJ, 115, 2285

Mahdavi A., Finoguenov A., Bohringer¨ H., Geller M. J., Henry J. P., 2005, ApJ, 622, 187

Mamon G. A., 1992, ApJL, 401, L3

Mantz A., Allen S. W., 2011, ArXiv e-prints

Mantz A., Allen S. W., Ebeling H., Rapetti D., Drlica-Wagner A., 2009a, ArXiv e-prints

Mantz A., Allen S. W., Ebeling H., Rapetti D., Drlica-Wagner A., 2010a, MNRAS, 406, 1773

Mantz A., Allen S. W., Rapetti D., Ebeling H., 2009b, ArXiv e-prints

Mantz A., Allen S. W., Rapetti D., Ebeling H., 2010b, MNRAS, 406, 1759

Marconi A., Hunt L. K., 2003, ApJL, 589, L21

Martini P. et al., 2013, ApJ, 768, 1

Martini P., Mulchaey J. S., Kelson D. D., 2007, ApJ, 664, 761

Martini P., Sivakoff G. R., Mulchaey J. S., 2009, ApJ, 701, 66

Maughan B. J., Giles P. A., Randall S. W., Jones C., Forman W. R., 2012, MNRAS, 421, 1583

McDonald M. et al., 2012, Nat, 488, 349

McDonald M. et al., 2013, ArXiv e-prints

McDonald M., Veilleux S., Rupke D. S. N., Mushotzky R., 2010, ApJ, 721, 1262

156 BIBLIOGRAPHY

McDonald M., Veilleux S., Rupke D. S. N., Mushotzky R., Reynolds C., 2011, ApJ, 734, 95

McNamara B. R., Nulsen P. E. J., 2007, ARA&A, 45, 117

McNamara B. R., Nulsen P. E. J., Wise M. W., Rafferty D. A., Carilli C., Sarazin C. L., Blanton E. L., 2005, Nat, 433, 45

Merloni A., Heinz S., 2008, MNRAS, 388, 1011

Million E. T., Allen S. W., 2009, MNRAS, 399, 1307

Million E. T., Allen S. W., Werner N., Taylor G. B., 2010a, MNRAS, 582

Million E. T., Allen S. W., Werner N., Taylor G. B., 2010b, MNRAS, 405, 1624

Million E. T., Werner N., Simionescu A., Allen S. W., 2011, MNRAS, 418, 2744

Mo H., van den Bosch F. C., White S., 2010, Galaxy Formation and Evolution

Moore B., Katz N., Lake G., Dressler A., Oemler A., 1996, Nat, 379, 613

Moore B., Kazantzidis S., Diemand J., Stadel J., 2004, MNRAS, 354, 522

Moore B., Lake G., Katz N., 1998, ApJ, 495, 139

Moore B., Lake G., Quinn T., Stadel J., 1999, MNRAS, 304, 465

Moran S. M., Ellis R. S., Treu T., Smail I., Dressler A., Coil A. L., Smith G. P., 2005, ApJ, 634, 977

Moretti A., Campana S., Lazzati D., Tagliaferri G., 2003, ApJ, 588, 696

Morgan W. W., 1961, Proceedings of the National Academy of Science, 47, 905

Nagai D., Kravtsov A. V., Vikhlinin A., 2007, ApJ, 668, 1

Napolitano N. R. et al., 2003, ApJ, 594, 172

Navarro J. F., Frenk C. S., White S. D. M., 1995, MNRAS, 275, 720

Navarro J. F., Frenk C. S., White S. D. M., 1997, ApJ, 490, 493

Nevalainen J., David L., Guainazzi M., 2010, A&A, 523, A22

157 BIBLIOGRAPHY

Nulsen P. E. J., 1982, MNRAS, 198, 1007

Nulsen P. E. J., McNamara B. R., Wise M. W., David L. P., 2005, ApJ, 628, 629

Nulsen P. E. J., Powell S. L., Vikhlinin A., 2010, ApJ, 722, 55

O’Dea C. P. et al., 2008, ApJ, 681, 1035

Oemler, Jr. A., 1974, ApJ, 194, 1

Oosterloo T., van Gorkom J., 2005, A&A, 437, L19

Owers M. S., Nulsen P. E. J., Couch W. J., Markevitch M., 2009, ApJ, 704, 1349

Palanque-Delabrouille N. et al., 2013, A&A, 551, A29

Pellegrini S., Ciotti L., Ostriker J. P., 2012, ApJ, 744, 21

Peterson J. R., Fabian A. C., 2006, Phys. Rep., 427, 1

Peterson J. R., Kahn S. M., Paerels F. B. S., Kaastra J. S., Tamura T., Bleeker J. A. M., Ferrigno C., Jernigan J. G., 2003, ApJ, 590, 207

Peterson J. R. et al., 2001, A&A, 365, L104

Pimbblet K. A., Shabala S. S., Haines C. P., Fraser-McKelvie A., Floyd D. J. E., 2013, MNRAS, 429, 1827

Popesso P., Biviano A., Bohringer¨ H., Romaniello M., 2007, A&A, 464, 451

Pratt G. W., Bohringer¨ H., Croston J. H., Arnaud M., Borgani S., Finoguenov A., Temple R. F., 2007, A&A, 461, 71

Puccetti S. et al., 2009, ApJS, 185, 586

Rafferty D. A., McNamara B. R., Nulsen P. E. J., 2008a, ApJ, 687, 899

Rafferty D. A., McNamara B. R., Nulsen P. E. J., 2008b, ApJ, 687, 899

Randall S., Nulsen P., Forman W. R., Jones C., Machacek M., Murray S. S., Maughan B., 2008, ApJ, 688, 208

158 BIBLIOGRAPHY

Rasera Y., Lynch B., Srivastava K., Chandran B., 2008, ApJ, 689, 825

Reichard T. A., Heckman T. M., Rudnick G., Brinchmann J., Kauffmann G., Wild V., 2009, ApJ, 691, 1005

Reis R. C., Miller J. M., 2013, ApJL, 769, L7

Rines K., Geller M. J., 2008, AJ, 135, 1837

Roediger E., Bruggen¨ M., 2006, MNRAS, 369, 567

Roediger E., Bruggen¨ M., 2007, MNRAS, 380, 1399

Roediger E., Bruggen¨ M., 2008a, MNRAS, 388, L89

Roediger E., Bruggen¨ M., 2008b, MNRAS, 388, 465

Roettiger K., Loken C., Burns J. O., 1997, ApJS, 109, 307

Ross N. P. et al., 2012, ArXiv e-prints

Ruderman J. T., Ebeling H., 2005, ApJL, 623, L81

Rudick C. S., Mihos J. C., Harding P., Feldmeier J. J., Janowiecki S., Morrison H. L., 2010, ApJ, 720, 569

Rudnick G. et al., 2009, ApJ, 700, 1559

Ruszkowski M., Bruggen M., Lee D., Shin M.-S., 2012, ArXiv e-prints

Rybicki G. B., Lightman A. P., 1985, Radiative processes in astrophysics.

Rykoff E. S. et al., 2013, ArXiv e-prints

Sanders J. S., 2006, MNRAS, 371, 829

Sanders J. S., Fabian A. C., 2002, MNRAS, 331, 273

Sanders J. S., Fabian A. C., 2007, MNRAS, 381, 1381

Sanders J. S., Fabian A. C., 2008, MNRAS, 390, L93

159 BIBLIOGRAPHY

Sanderson A. J. R., Ponman T. J., O’Sullivan E., 2006, MNRAS, 372, 1496

Schmidt R. W., Allen S. W., 2007, MNRAS, 379, 209

Schure K. M., Kosenko D., Kaastra J. S., Keppens R., Vink J., 2009, A&A, 508, 751

Scoville N. et al., 2007a, ApJS, 172, 38

Scoville N. et al., 2007b, ApJS, 172, 1

Shemmer O., Brandt W. N., Netzer H., Maiolino R., Kaspi S., 2008, ApJ, 682, 81

Shemmer O., Brandt W. N., Vignali C., Schneider D. P., Fan X., Richards G. T., Strauss M. A., 2005, ApJ, 630, 729

Sidher S. D., Sumner T. J., Quenby J. J., Gambhir M., 1996, A&A, 305, 308

Silk J., Rees M. J., 1998, A&A, 331, L1

Silverman J. D. et al., 2008, ApJ, 679, 118

Simionescu A., Werner N., Bohringer¨ H., Kaastra J. S., Finoguenov A., Bruggen¨ M., Nulsen P. E. J., 2009, A&A, 493, 409

Smith R. J., Lucey J. R., Hudson M. J., Schlegel D. J., Davies R. L., 2000, MNRAS, 313, 469

Snowden S. L., Mushotzky R. F., Kuntz K. D., Davis D. S., 2008, A&A, 478, 615

Springel V. et al., 2005, Nat, 435, 629

Sun M., Donahue M., Voit G. M., 2007, ApJ, 671, 190

Sutherland R. S., Dopita M. A., 1993, ApJS, 88, 253

Tal T., van Dokkum P. G., Nelan J., Bezanson R., 2009, AJ, 138, 1417

Taniguchi Y. et al., 2007, ApJS, 172, 9

Taylor G. B., Barton E. J., Ge J., 1994, AJ, 107, 1942

Taylor G. B., Fabian A. C., Allen S. W., 2002, MNRAS, 334, 769

160 BIBLIOGRAPHY

Tonnesen S., Bryan G. L., 2010, ApJ, 709, 1203

Tonnesen S., Bryan G. L., 2012, MNRAS, 422, 1609

Tonnesen S., Bryan G. L., Chen R., 2011, ApJ, 731, 98

Trager S. C., Faber S. M., Worthey G., Gonzalez´ J. J., 2000, AJ, 119, 1645

Truemper J., 1993, Science, 260, 1769

Tsujimoto M. et al., 2011, A&A, 525, A25

Ueda Y., Akiyama M., Ohta K., Miyaji T., 2003, ApJ, 598, 886

Urban O., Werner N., Simionescu A., Allen S. W., Bohringer¨ H., 2011, MNRAS, 414, 2101 van den Bosch F. C., Aquino D., Yang X., Mo H. J., Pasquali A., McIntosh D. H., Weinmann S. M., Kang X., 2008a, MNRAS, 387, 79 van den Bosch F. C., Pasquali A., Yang X., Mo H. J., Weinmann S., McIntosh D. H., Aquino D., 2008b, ArXiv e-prints

Vikhlinin A. et al., 2009, ApJ, 692, 1060

Vikhlinin A., Markevitch M., Murray S. S., Jones C., Forman W., Van Speybroeck L., 2005, ApJ, 628, 655

Voit G. M., Kay S. T., Bryan G. L., 2005, MNRAS, 364, 909 von der Linden A. et al., 2012, ArXiv e-prints:#1208.0597 von der Linden A., Best P. N., Kauffmann G., White S. D. M., 2007, MNRAS, 379, 867 von der Linden A., Wild V., Kauffmann G., White S. D. M., Weinmann S., 2010, MNRAS, 404, 1231

Weinmann S. M., Kauffmann G., van den Bosch F. C., Pasquali A., McIntosh D. H., Mo H., Yang X., Guo Y., 2009, MNRAS, 394, 1213

Weinmann S. M., van den Bosch F. C., Yang X., Mo H. J., Croton D. J., Moore B., 2006, MNRAS, 372, 1161

161 BIBLIOGRAPHY

Weisskopf M. C., Wu K., Trimble V., O’Dell S. L., Elsner R. F., Zavlin V. E., Kouveliotou C., 2007, ApJ, 657, 1026

Werner N., Durret F., Ohashi T., Schindler S., Wiersma R. P. C., 2008, Space Sci. Rev., 134, 337

Werner N. et al., 2012, ApJ submitted [arXive:1211.6722]

Werner N. et al., 2010, MNRAS, 407, 2063

Werner N. et al., 2011, MNRAS, 415, 3369

Wetzel A. R., Tinker J. L., Conroy C., 2012, MNRAS, 424, 232

White D. A., Fabian A. C., Forman W., Jones C., Stern C., 1991, ApJ, 375, 35

White D. A., Jones C., Forman W., 1997, MNRAS, 292, 419

Xue Y. Q. et al., 2010, ApJ, 720, 368

Xue Y. Q. et al., 2011, ApJS, 195, 10

Zdziarski A. A., Poutanen J., Johnson W. N., 2000, ApJ, 542, 703

Zhao J., Sumi D. M., Burns J. O., Duric N., 1993, ApJ, 416, 51

ZuHone J. A., Markevitch M., Johnson R. E., 2009, ArXiv e-prints

162

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