Globular Cluster Kinematics and Dark Matter Content Of

Globular Cluster Kinematics and Dark Matter

Content of the Isolated Elliptical NGC 720

Amanda M. Schembri

A thesis submitted to the Department of Physics, Astronomy and Engineering Physics in conformity with the requirements for the degree of Master of Science

Queen’s University Kingston, Ontario, Canada January 2011

We examine the globular cluster system (GCS) of the isolated elliptical NGC 720 using the Gemini Multi-Object Spectrograph (GMOS) and have obtained spectra for 241 candidate globular clusters (GCs) extending to a galactocentric radius of 40 kpc. Of the 241 candidates, 120 are conﬁrmed GCs, where 46 are members of the metal-poor, blue, population and 74 are members of the metal-rich, red, population. A (g-i) =0.50 colour split is used to identify the blue and red populations. We measure the full GCS to have a rotational velocity of 50 ± 7 km/s with a position

◦ ◦ angle of θ0 = 170 ± 69 . The red population has a rotational velocity of 97 ± 14

◦ km/s with θ0 = 147 ± 18 and the blue population has Vrot = 79 ± 7 km/s with

◦ θ0 = 89 ± 18 . The full GCS has an average velocity dispersion of 168 ± 22 km/s, for the red population is 156 ± 30 km/s and for the blue population is 181 ± 33 km/s. The velocity dispersion proﬁle for all populations is constant with increasing radius, suggesting the presence of a dark matter halo. Using a tracer mass estimator, we

+0.6 12 have measured the mass out to 40 kpc as 1.8−0.1 × 10 M for a potential which traces the dark matter proﬁle. We also estimate the M/LV = 30 − 70. This study extends our survey of GCSs to isolated environments.

i Acknowledgments

I would like to thank Dave Hanes for taking me on as a student, for taking me to Chile to visit Gemini South and for all his help and insight! A huge thank you goes to Terry Bridges for all the advice he has given me, for all the hours teaching me how to reduce my data and for the effort put into helping me edit this thesis. This project would not have progressed as it did without his guidance! A huge thank you goes out to all the astro grads - for all the official (and unofficial) tea times, birthday cake celebrations, grad club visits, assignment parties, real parties, ”magical food adventures” and ridiculous lunch time conversations. They were a blast and, well, magical! I would like to thank my fiance, Viktor Terlaky, for making me laugh, and for dragging me away from my desk when I refused to take breaks. He, somehow, managed to keep me sane... for the most part at least! I’d like to thank my parents for encouraging me to always persevere and to put my full effort into everything I put my mind to. Finally, I would like to thank the KMS triathalon group I coached for being so enthusiastic about learning and for putting on a smile when I put them through their paces. I learnt as much from them as (I hope) they did from me. Thank you Queen’s swimming pool for existing and letting me take my frustration and excess energy out on you. Thank you everyone!

ii Table of Contents

Abstract i

Acknowledgments ii

Table of Contents iii

List of Tables v

List of Figures vi

1 Introduction 1 1.1 Motivation ...... 1 1.2 Contributions ...... 1 1.3 Organization of Thesis ...... 2

Chapter 2: Background on Globular Cluster Systems ...... 3 2.1 General ...... 3 2.2 Bimodality in Globular Clusters ...... 4 2.3 GCS and Formation History ...... 6 2.4 Dynamical Tracers ...... 10 2.5 X-ray Observations ...... 12 2.6 Speciﬁc GCS Studies ...... 13

Chapter 3: NGC 720 ...... 19 3.1 Overview of Properties ...... 19 3.2 X-ray Observations ...... 23 3.3 The Isophotes of NGC 720 ...... 25 3.4 Previous Determinations of the Mass of NGC 720 ...... 26

iii Chapter 4: Data Reduction ...... 28 4.1 The Data ...... 28 4.2 The Gemini Multi-Object Spectrograph ...... 33 4.3 The Reduction ...... 33 4.4 The Spectra ...... 41 4.5 Astrometry ...... 41 4.6 Photometry ...... 52 4.7 Cross-Correlation ...... 61 4.8 Repeat Observations ...... 78

Chapter 5: Modeling ...... 82 5.1 Rotation in the GCS of NGC 720 ...... 82 5.2 Velocity Dispersion ...... 95 5.3 Tracer Mass Estimator ...... 100

Chapter 6: Discussion ...... 108 6.1 GC Rotation ...... 108 6.2 Velocity and Velocity Dispersion of the GCS ...... 112 6.3 Mass ...... 116 6.4 Implications ...... 119

Chapter 7: Summary and Conclusions ...... 122 7.1 Summary ...... 122 7.2 Future Work ...... 123 7.3 Conclusion ...... 124

Appendix A: Tables of Data ...... 134

iv List of Tables

3.1 Properties of NGC 720 ...... 20

4.1 Location of the fields used for spectroscopy of NGC 720 ...... 31 4.2 Astrometry matches between fields ...... 79 4.3 Velocity matches between fields ...... 80

5.1 Summary for Results Obtained from Wrap.f90 and LoopyWrap.f90 code 89 5.2 Summary of isotropic and anisotropic mass estimates for varying α and β parameters. We set γ = 2.82 ...... 105 5.3 Summary of averaged masses for varying β and constant α ...... 107

6.1 Summary of kinematics data from other the GCSs of elliptical galaxies. References are as follows: [1] = Côté et al. [2001], [2] = Woodley et al. [2010], [3] = Côté et al. [2003], [4] = Bridges et al. [2006], [6] = Hwang et al. [2008], [5] = Schuberth et al. [2010]...... 111 6.2 Average velocities corresponding to azimuthal angles and Figure 6.1 . 113 6.3 Summary of past estimates of the mass and M/L of NGC 720. . . . . 118

A.1 Photometry Results: In the Comments column, R and C indicate whether an object is rejected (R) from candidacy or is confirmed (C) as a GC. IP and GP indicate that there were problems obtaining photometry for either the i-band or g-band image...... 134 A.2 Velocity Results: The Quality column comments on the quality of the cross-correlation fit where G = good quality, P = poor, Pf = initially poor but has been fixed. The Comments column states whether a spectra is a confirmed (C) GC or if it was rejected (R)...... 140 A.3 Astrometry Results: The i-band and g-band astrometry is included even though all the analysis was performed using the g-band data. The comments column specifies whether an object was confirmed (C) as a GC or rejected (R)...... 146

v List of Figures

2.1 Colour magnitude diagram on the left and corresponding colour distribution for diﬀerent magnitude ranges on the right for the M 87 GCS. There is a clear distinction between the blue and red population [Harris , 2009]...... 5

4.1 The five fields for NGC 720. Field 1 is the center green box, Field 2 is the green box to the NW, Field 3 is the blue box to the SE, Field 4 is red box to the NE and Field 5 is the red box to the SW. The RA coordinate increases to the left...... 32 4.2 A sample arc calibration where the initial 8 emission lines are identified. These lines are identified by the user and then the gswavelength task uses these to autoidentify the rest. The x-axis is the wavelength in angstroms and the y-axis is in counts...... 38 4.3 Examples of confirmed GCs with low S/N. The spectrum on the left has a S/N = 2.9 and on the right, S/N =3.5...... 42 4.4 Examples of confirmed GCs with medium S/N. The spectrum on the left has a S/N = 13.0, and on the right, S/N = 11.8. In both figures, Hβ is visible at 486.1 nm and the Mg B lines are distinctly visible at 517.2 nm and 518.3 nm only for the figure on the right...... 42 4.5 Examples of confirmed GCs with high S/N. The spectrum on the left has a S/N = 15.5, and on the right, S/N = 28.8. Hβ is visible at 486.1 nm and the Mg B lines are visible at 517.2 nm and 518.3 nm. . 43 4.6 Examples of unusual spectra. The spectrum on the left has S/N = 14.1, and on the right, S/N =64.5...... 43 4.7 Spectrum of the center of NGC 720...... 44 4.8 Field 1 Image in g-band. 18 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering...... 46

vi 4.9 Field 2 Image in g-band. 18 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering...... 47 4.10 Field 3 Image in g-band. 22 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering...... 48 4.11 Field 4 Image in g-band. 15 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering...... 49 4.12 Field 5 Image in g-band. 15 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering. The OIWFS arm can be seen on the right side of the image...... 50 4.13 The positions of all objects with the geomap transformation applied. The crosshairs mark the centre of NGC 720. Black circles mark acquisition objects. Blue squares mark Field 1 objects, green triangles mark Field 2 objects, red pentagons mark field 3 objects, magenta triangles mark field 4 objects and cyan squares mark field 5 objects. . 53 4.14 The same figure as Figure 4.13 but with the original untransformed positions marked as hollow shapes. The filled shapes mark the transformed positions as in Figure 4.13. Again, the crosshairs mark the centre of NGC 720. Black circles mark acquisition objects. Blue squares mark Field 1 objects, green triangles mark Field 2 objects, red pentagons mark field 3 objects, magenta triangles mark field 4 objects and cyan squares mark field 5 objects...... 54 4.15 The coordinates of the candidate globular clusters shown as blue squares. Rejected data are shown as magenta triangles...... 55 4.16 The same data as Figure 4.15 but with colour information. Blue cluster candidates are shown as blue squares and red clusters as red pentagons. Again, rejected data are magenta...... 56 4.17 Colour distribution of NGC 720 in blue with the generalized histogram fit superimposed in black. The blue peak at (g-i) = 0.4, and the red peak at (g-i) = 0.6. The vertical red line marks the subdivision between the populations at (g-i) = 0.50...... 62

vii 4.18 The 24 templates used for the cross-correlation. The spectra from left to right go from ages 1.78, 3.98, 8.91 to 12.59 Gyr. From top to bottom the metallicity decreases beginning with +0.20, 0.00, -0.38, -0.68, -1.28 and -1.68 at the bottom. The wavelength in angstroms is at the bottom of the figure...... 64 4.19 The weighted mean of the heliocentric velocity plotted against the Tonry & Davis R-value for the whole sample set (241 objects). The blue and red populations are shown by blue and red colours respectively. The colour division occurs at (g-i) = 0.50 (see Section 4.6.1) . 71 4.20 The distribution of the weighted mean of the heliocentric velocity for the whole sample set. The vertical black line marks the systemic velocity of NGC 720...... 72 4.21 The weighted mean of the heliocentric velocity plotted against the Tonry & Davis R-value for the whole sample size (241 objects). The blue and red populations are shown by blue and red colours respectively. The colour division occurs at (g-i) = 0.50 (see Section 4.6.1). Error bars are included in this figure. A TDR value less than 5 (the black vertical line) denotes a poor cross-correlation...... 73 4.22 The weighted mean of the heliocentric velocity plotted against the Tonry & Davis R-value for confirmed globular clusters. The blue and red populations are shown by blue and red colours respectively. The colour division occurs at (g-i) = 0.50 (see Section 4.6.1). A TDR value less than 5 (the black vertical line) denotes a poor cross-correlation. . 74 4.23 The distribution of Tonry & Davis R-value counts. Confirmed objects are shown in green (120 confirmed GCs) and rejected objects (115 objects) in magenta...... 76 4.24 The velocity distribution centered on the candidacy region. Confirmed objects are shown in green and rejected objects in magenta. The number of objects within each sample is shown in the top right corner. 77

5.1 Diagram for azimuthal angle calculations, where the origin, O, is the center of NGC 720 at (28.252167, -13.738667). Point G represents a GC with some position (a,b), and point P is the azimuth at (28.252167, 0)...... 83 5.2 The distribution of azimuthal angle for conﬁrmed GCs in degrees using 20◦ bins. The vertical dotted line marks 360◦ where we fold the data to demonstrate periodicity...... 84

viii 5.3 Velocity of confirmed GCs vs. azimuthal angle. The colour subdivision is made at (g-i) = 0.50. The blue squares represent blue GCs and the red triangles represent red GCs. The horizontal dashed line marks the systemic velocity of NGC 720 at 1745 km/s. The vertical dotted line marks 360◦ where the data are folded...... 85 5.4 The %-Difference for the whole data sample. The horizontal line marks the 0% level. The vertical line is at a Vrot = 15 km/s which is where the results asymptotically begin to increase...... 92 5.5 The %-Difference for the red data sample. The horizontal line marks the 0% level. The vertical line is at a Vrot = 15 km/s which is where the results asymptotically begin to increase...... 93 5.6 The %-Difference for the blue data sample. The horizontal line marks the 0% level. The vertical line is at a Vrot = 10 km/s which is where the results asymptotically begin to increase...... 94 5.7 Velocity vs. azimuthal angle for the final fits of the rotational velocity and position angle. The blue and red points represent the GCs. The red curve shows the best fit for Vrot = 97 ± 14 km/s for the red GCs, the blue curve shows the best fit for Vrot = 79 ± 7 km/s for the blue GCs and the green curve shows the best fit for the whole GCS with Vrot = 50 ± 7 km/s. The horizontal dashed line marks the systemic velocity of NGC 720...... 96 5.8 Velocity vs. galactocentric radius for lowess fits with f = 0.3 for the red and full populations and f= 0.5 for the blue population. The bottom window is the fit to the whole sample, the middle window is for the blue GCs and the top window is for the red GCs. The solid black curve is the lowess fit. The horizontal line is the systemic velocity. . . 98 5.9 The velocity dispersion (|∆σ|) vs galactocentric radius for a f=0.3 for the red and full population and f = 0.5 for the blue population. The bottom window is the lowess fit for all the data. The middle window is for the blue GCs and top window is for the red GCs...... 99 5.10 Surface brightness profile of NGC 720 in V-band using stellar data from Goudfrooij et al. [1994] in green and GCs data from Kissler- Patig et al. [1996] in blue. The red line is the fit to the data to obtain a measure of γ...... 102

ix 5.11 The distribution of masses determined by the tracer mass code for 1000 diﬀerent datasets. The blue distribution represents the isotropic masses, the green distribution represents the anisotropic masses and the red distribution represents the projected masses. The top row of plots are for α = 0.0, the middle row for α = γ − 2 and the last row for α = 0.2. The ﬁrst column is for β = 0.4 and the second column is for β = 0.8...... 106

6.1 The positions of the conﬁrmed GCs with colour and velocity information. The large blue circles mark positions of blue GCs and the large red circles mark the positions of red GCs. The black points and crosses denote velocity information. A cross marks a GC with a velocity > 1746 km/s and a point marks a GC with a velocity < 1745 km/s. The dotted horizontal and vertical lines mark the centre of the galaxy...... 114

x Chapter 1

Introduction

1.1 Motivation

Bimodal colour populations of globular clusters have been observed in essentially all galaxies which have accurate photometry, suggesting bimodality is a normal property within galactic evolution. The exact origin of this bimodal distribution is still un- known and under heavy discussion. It is theorized that the source of this bimodality is from two epochs of star formation. The first epoch occurred when the galaxy was forming. The metal-poor, blue, globular clusters may have formed at this time. The second epoch occurs some time later after the metallicity of the interstellar medium increased. Some sort of event caused a second burst in star formation to produce the metal-rich, red, globular clusters. One possible mechanism that initiates a burst of star formation is a merger event or interaction. If this theory is correct, then the kinematics of the globular clusters may be able to reveal the properties of this event. The mass profile and total mass of the dark matter halo is also of interest to us. Previous studies of NGC 720 have determined that it does have a dark matter halo, but data have been insufficient to determine any more information than this. X-ray observations and stellar kinematics studies have probed the gravitational potential at large and small galactocentric radii, respectively. This study of the GCS will provide a dynamical tracer to fill in the gap between them.

1.2 Contributions

The data in this thesis are part of a spectroscopic survey which is investigating the kinematics of globular cluster systems in diﬀerent environments. Many previous

1 CHAPTER 1. INTRODUCTION 2

studies have focused on the globular cluster systems of galaxies within galaxy groups. This study of the globular cluster system of NGC 720 extends this survey to an isolated environment. The research group and clusters are: Dave Hanes (Queen’s University), Terry Bridges (Queen’s University), Favio Faifer (Facultad de Cs. Astronomicas y Ge- ofisicas, UNLP), Duncan Forbes (Swinburne University of Technology), Juan Forte (Facultad de Cs. Astronomicas y Geofisicas, UNLP), Karl Gebhardt (University of Texas at Austin), Mark Norris (University of North Carolina), Ray Sharples (Uni- versity of Durham), Steve Zepf (Michigan State University) This group was involved in the telescope time proposals to obtain the data presented in this research. Significant aid was provided by Dave Hanes, my supervisor and Terry Bridges who provided expertise primarily on data reduction. Karl Gebardt provided me with several codes to fit the data; these are discussed in the thesis. The data reduction and analysis I performed myself.

1.3 Organization of Thesis

This thesis is organized as follows: Chapter 2 is a literature review of the relevant literature of globular cluster systems and of galaxy formation models. Chapter 3 summarizes the properties and past observations of NGC 720. Chapter 4 describes the data reduction process. Chapter 5 presents results for GC rotation, velocity dispersion of the GCS and dark matter content of NGC 720. The discussion of the results is in Chapter 6. Chapter 7 summarizes the results and gives suggestions for future work. Chapter 2

Background on Globular Cluster Systems

2.1 General

Globular clusters (GCs) are massive, gravitationally bound clusters of stars that can contain anywhere from tens of thousands of stars to hundreds of thousands of stars. Their ages can range from a few Gyr, to being the oldest stellar structures in the universe. The younger GCs are formed during mergers and starbursts. They have

4 6 masses ranging from ∼ 10 - 10 M and absolute magnitudes of MV = -5 to -10 [Hanes et al., 2001, Perrett et al., 2002, Brodie & Strader, 2006]. The population of GCs within a galaxy, or globular cluster system (GCS) is excellent tool to study an array of properties of the GC host galaxy. The chemical composition and kinematics of the GCS, for example, are used to study the star formation history, and evolution

3 CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 4

of the host galaxy [Hanes et al., 2001, Perrett et al., 2002, Bridges et al., 2003, Brodie & Strader, 2006]. GCs kinematically trace the gravitational potential of their host galaxy. Kinematics, therefore, reveal information about the dark matter content and distribution around and within a galaxy [Schuberth et al., 2010]. Elliptical galaxies, such as NGC 720, the subject of this study, host signiﬁcantly more GCs than spirals, making elliptical galaxies prime targets for kinematics studies [Schuberth et al., 2010]. The galaxies at the center of galaxy clusters are found to host the largest GCSs of all, of which M87 in Virgo and NGC 1399 in Fornax are the closest examples [Schuberth et al., 2010].

2.2 Bimodality in Globular Clusters

The first galaxies with measured bimodal or multimodal colour populations were NGC 4472 and NGC 5128 by Zepf & Ashman [1993], and NGC 1399 by Ostrov, Geisler, & Forte [1993] [Brodie & Strader, 2006]. Bimodality is exhibited through the colour differences of two (or more) distinct populations within a GCS: a metal- rich, red, population and a metal-poor, blue, population. Figure 2.1 shows clearly this bimodality for the GCS of M 87. The GCS of M 87 has a blue peak, fit by a Gaussian distribution, at (g-i)=0.80 and a red peak at (g-i)=1.07 [Harris , 2009]. The source of these distinct populations is ambiguous in photometry because of the age-metallicity degeneracy: it cannot be determined whether the populations differ in age, metallicity, or a combination of both [Worthey, 1994]. Spectroscopic studies are required to break this degeneracy, demonstrating that differences in metallicity CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 5

are primarily responsible for the colour differences [Brodie & Strader, 2006]. The two distinct populations are also observed to have different chemical abundances, and different kinematics [Hanes et al., 2001]. The red and blue populations are commonly separated to study their dynamics and chemical properties [Hanes et al., 2001]. The specific colour of the bimodal peaks are unique for each galaxy [Faifer et al., 2010].

Figure 2.1: Colour magnitude diagram on the left and corresponding colour distribution for diﬀerent magnitude ranges on the right for the M 87 GCS. There is a clear distinction between the blue and red population [Harris , 2009].

Measuring bimodality is diﬃcult because it requires both high spatial resolution and high-quality photometry; the Wide-Field Planetary Camera 2 and the Advanced CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 6

Camera for Surveys on the Hubble Space Telescope have been assets in these studies [Brodie & Strader, 2006]. Presently, every observation with high-quality photometry of massive galaxies shows a bimodal GC population [Gebhardt & Kissler-Patig, 1999, Kundu & Whitmore, 2001, Harriset al., 2006]. Current technology allows bimodality to be detected to roughly the distance of the Virgo cluster at 17 Mpc [Brodie & Strader, 2006].

2.3 GCS and Formation History

Bimodal GC populations provide strong evidence for galaxies forming episodically in two phases [Zepf et al., 2000, Brodie & Strader, 2006]. In the first phase the metal- poor GCs form in situ during the formation of the host galaxy [Zepf et al., 2000]. The dynamics of this population may be dependent on the merger history of the galaxy [Brodie & Strader, 2006]. The metal-rich GCs are thought to form later, in a second phase, after the interstellar medium was enriched. The mechanisms initiating these phases depend on the formation model invoked. Monolithic formation models have difficulty independently explaining bimodality because they require a mechanism to turn off GC formation between phases. Hierarchical models however, are able to explain bimodality quite elegantly. Galaxies grow through merging in hierarchical models. The metal-rich population is therefore the product of a major star forming epoch, possibly initiated by a wet merger. The bulk of metal-rich field stars will form at this epoch as well. These metal-rich GCs naively, should trace the galaxy light. [Brodie & Strader, 2006]. The amount of gas introduced during the interaction is CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 7

proportional to the number of newly formed clusters. The age of the red population can thus be determined by examining the metallicity of the GCs [Kissler-Patig et al., 1998]. The metallicity corresponds to the metallicity of the raw materials at the time of the merger event. It is in this way that GCs can be used to trace the star formation history. The merger model explains the metal-rich GC population as forming during the merger of two gas-rich spirals, each containing a metal-poor GC population only [Ashman & Zepf, 1992]. This traditional merger model has difficulty explaining the production of sufficient numbers of metal-poor GCs [Zepf et al., 2000]. A good example of this problem is exhibited by M 87, which has a significantly larger population of metal-poor GCs than metal-rich GCs (see Figure 2.1). If the Milky Way and M 31 were involved in a hypothetical spiral-spiral merger, then the total combined number of metal-poor GCs would still not approach the numbers in M 87. Ellipticals can also have a relatively small metal-poor population, or have too many or too few metal-rich GCs [Zepf et al., 2000]. This problem can be remedied by requiring the progenitor spirals host a larger or smaller population of GCs, or by the dissipationless accretion of a neighboring lower-mass galaxy to acquire more metal-poor GCs [Côté et al., 1998]. If a galaxy did not form through mergers, but instead slowly accreted through minor mergers, then, assuming GCs were created in the interaction, they would have a gradually increasing metallicity [Pierce et al., 2006a]. The multi-phase dissipational collapse model by Forbes et al. [1997] is able to produce a bimodal colour distribution with GC numbers consistent with observations. They assume the metal-poor GCs form early in galaxy formation and then CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 8

GC formation is truncated at high redshift (approximately z > 5 in order to recre- ate the necessary bimodality). Later on, the metal-rich population and field stars are formed via a gas-rich encounter of some sort. Beasley et al. [2002] use age to determine the validity of a similar model, which predicts that red, metal-rich GCs should be 2-4 Gyr younger than the blue, metal-poor GCs. This would also indicate that the GCSs of different galaxies would have similar age differences [Beasley et al., 2002]. There is also the problem of managing angular momentum in formation models. In terms of the visible components of galaxy dynamics, spirals are over a magnitude more rotation dominated than ellipticals [Silk & Wyse, 1993, Zepf et al., 2000]. Not all ellipticals, however, have significant rotation. If ellipticals were to form from the merging of two spirals, then the acquisition of angular momentum is critical in formation scenarios. Extensive research has gone into understanding how orbital and spin angular momentum is converted under the various formation theories: monolithic collapse [Peebles , 1969], major mergers [Heyl et al., 1996] and multiple mergers [Weil & Hernquist, 1996, Côté et al., 2001]. This angular momentum is linked to GC studies through the rotation properties of distinct GC populations. Either different, or similar, rotational properties can reveal much information about the formation of the galaxy and can help determine the best formation model [Côté et al., 2001]. The rotation of GCS in the progenitor galaxy and in the end result can therefore provide great insight to the formation history of the galaxy. Galaxy formation models all require that the metal-rich population forming via dissipative collapse acquires CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 9

significant rotation. If angular momentum is transported outwards by some mechanism, then a high rotation for metal-rich GCs can be avoided. In merger simulations, angular momentum transport is efficient enough to do this; however, dissipative collapse models are not [Zepf et al., 2000]. The acquisition of angular momentum under the monolithic collapse model is problematic because in this model it can only arise from the tidal torques of companions [Peebles , 1969, Côté et al., 2001]. The best formation model, in all likelihood, is a combination of hierarchical growth/accretion, merging and monolithic collapse. Various types of mergers were simulated to study GCS by Bekki et al. [2005]. Bekki et al. [2005] observed that the metal-rich clusters that formed were more centrally concentrated with their distribution extending along the major axis for spiral-spiral mergers. This causes the rotation of the metal-poor GCs to increase and extends the spatial distribution to larger radii. The simulation results are highly dependent on the initial conditions [Bekki et al., 2005]. The end products of mergers between purely stellar disks have cores significantly different from those observed at the center of galaxy clusters. This is not a likely mechanism, because the resulting galaxy usually has a Hubble type of E3-E7 [Hernquist , 1992]. Most ellipticals have Hubble types closer to E2 [Franx et al., 1991]. Commonly, the merger product also has a large misalignment between the minor axis and rotation axis, which is problematic, because extreme misalignments are rare [Weil & Hernquist, 1996, Heyl et al., 1996]. Smaller misalignments, however, are quite common [Franx et al., 1991, Côté et al., 2001]. If gas is included in the simulations, then the resulting galaxy is quite different. The misalignment between the minor and rotation axis is much smaller, and the central density is higher. These CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 10

results, however, are dependent on the cooling used in the simulation [Barnes & Hernquist, 1996, Cˆot´e et al., 2001].

2.4 Dynamical Tracers

Current theories under ΛCDM indicate that the dark matter of galaxies must be immense. The dark matter potential can be probed through the use of dynamical tracers. Dynamical tracers include GCs, planetary nebulae (PNe) and stellar kinematics. Stellar kinematics are of limited use because they trace the inner 2-3Reff of galaxies, and thus diminish just as the dark matter halo begins to dominate. Ground-based 8 m telescopes have made it possible to observe GCSs in detail. GCs are excellent tracers because they are sufficiently distant to serve as luminous point sources, they are numerous, and they orbit at large radii where the surface brightness of the halo is extremely faint [Hanes et al., 2001]. GC tracers compliment other tracers such as stellar kinematics and PNe, because these data can be combined to obtain a more complete snapshot of the kinematics of a GCS [Bridges et al., 2006]. Researchers are trying to increase the number of GC velocities obtained. Sample sizes are too small compared to the ideal sample of over one thousand velocities, which Merritt & Tremblay (1993) predict is necessary to self-consistently constrain both the velocity anisotropy and mass distribution of a galaxy [Côté et al., 2003]. CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 11

2.4.1 Planetary Nebulae as Dynamical Tracers

The distribution of PNe also extends to large radii. PNe can be identified by their strong OIII emission lines at 4959/5007A˚ [Bridges et al., 2006]. Using PNe as tracers would complement GCs tracers [Côté et al., 2001, Bridges et al., 2006], but the velocities of PNe are puzzling. Romanowsky et al. [2003] measured the velocity dispersion profiles of NGC 821, NGC 3379 and NGC 4494 and found that they declined with distance, which is contrary to our current understanding of dark matter halos. Numerical simulations of spiral-spiral mergers by Dekel et al. [2005] indicate that the velocity dispersion of PNe may be lower than what the underlying dark matter halo should reflect. These highly radial orbits can be explained if is that the progenitor stars are ejected from inner orbits during interactions and mergers [Dekel et al., 2005]. The velocity dispersion of GCs is larger, therefore they are a better measure of the underlying dark matter distribution [Brodie & Strader, 2006]. Evidence from radial velocity surveys by Hui et al. [1995], Arnaboldi et al. [1996, 1998] suggest than PNe in general may have rapid rotation at large radii within giant ellipticals [Côté et al., 2001].

2.4.2 Stellar Kinematics as Dynamical Tracers

Absorption-line rotation curves and stellar proﬁles are used to measure stellar kinematics [Fisher et al., 1995]. The line strength gradients in a galaxy spectrum is correlated with several kinematic properties. The Mg line strengths in ellipticals, for example, are well correlated with the velocity dispersion at small radii [Fisher et al., 1995]. The spectra of stars can also be used to break the age-metallicity degeneracy CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 12

and reveal any metallicity or age gradients within the galaxy [Fisher et al., 1995]. If a galaxy formed under the dissipative formation model then stars, which would have formed quickly, would be dynamically hot, the gradient of the velocity dispersion would be small and the abundance gradients would be shallow. If there was more dissipation during galaxy formation, then the stars would form more slowly, be dynamically cool, have larger gradients in the velocity dispersion and would have deeper line strengths. Stellar kinematics studies have the advantage of not suﬀering from poor sample sizes as is the case for GCs and PNe studies [Forestell & Geb- hardt, 2010]. Stellar kinematics are better able to probe the underlying gravitational potential at small radii [Forestell & Gebhardt, 2010].

2.5 X-ray Observations

X-ray observations are able to probe further into the dark matter halo than GCs. Extended dark halos seem to be a common feature in luminous ellipticals according to high-quality data obtained from ROSAT, ASCA and Chandra [Humphrey et al., 2006, Bridges et al., 2006]. X-ray observations obtain excellent data out to large radii, however, galaxies must have sufficient hot, diffuse gas to provide high-quality gas density and temperature profiles. Hot diffuse gas is only present in massive ellipticals; X-ray observations are thus restricted to these galaxies [Bridges et al., 2006]. Accurate measurements require an assumption of hydrostatic equilibrium which may not always be appropriate. CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 13

2.6 Speciﬁc GCS Studies

2.6.1 NGC 1399

NGC 1399 is the dominant galaxy of the nearby Fornax cluster and has one of the largest known GCS populations. The current dataset is comprised of over 700 GCs spanning a radii from 6-100 kpc [Richtler et al., 2004, Schuberth et al., 2010]. The red and blue populations have distinct colour and kinematics. The red population follows the stellar population distribution. The distribution of the blue GCs is more erratic and more spatially extended. The red clusters show no signiﬁcant rotation while the blue clusters show a very weak rotation between radii of 22-44 kpc [Schuberth et al., 2010]. The velocity dispersion of the blue population is 290 km/s and is 255 km/s for the red population [Brodie & Strader, 2006]. The velocity distribution suggests orbital isotropy [Schuberth et al., 2010]. Schuberth et al. [2010] concluded that the blue GCs were accreted during formation of the galaxy cluster. The red population was used to model the dark matter halo, because their dynamical history is similar to that of the galaxy [Richtler et al., 2004, Schuberth et al., 2010].

2.6.2 NGC 4649 (M60)

NGC 4649 is a cD galaxy in the Virgo cluster. Bridges et al. [2006] performed multi- object spectroscopy to measure 38 GC velocities extending to a radius of 260” (or

Reff = 3.5). They determined the velocity dispersion of the whole GC population is constant with radius. No evidence of rotation was found in the blue, red or whole CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 14

population. This supports observations suggesting that NGC 4649 has never undergone a major merger and instead has accreted material over time. Using spherical, isotropic, axisymmetric, orbit-based dynamical models they ﬁnd evidence for a dark matter halo.

2.6.3 NGC 4486 (M 87)

M 87 is the central giant elliptical galaxy of the Virgo Cluster and is thought to host 13500 GCs [Côté et al., 2003]. Using multi-object spectroscopy from the CFHT, Hanes et al. [2001] measured 96 new GC velocities bringing the total sample size of the GCS to 352. Côté et al. [2001] found that the 161 blue clusters are more spatially extended than the 117 red clusters. The blue GCs dominate outside a radius of 10 kpc. They measure the rotational velocity for the whole GCS, the blue GCs and the red GCs and find that each population has a rotational amplitude of roughly 160- 170 km/s [Côté et al., 2001, 2003, Schuberth et al., 2010]. The velocity dispersion of both populations are not significantly different from each other. The blue clusters have a dispersion of 365 km/s and the red clusters have a dispersion of 395 km/s [Côté et al., 2001, Schuberth et al., 2010]. The rotation curve suggests that the metal-rich GCs are rotating about the photometric minor axis, while the metal-poor clusters are rotating about the photometric major axis at 65◦. The two dimensional smoothed line-of-sight-velocity residuals appear to have a ”double lobbed” pattern with an orientation close to the photometric major axis. The metal-poor GCs show approximate solid-body rotation with a slight ”shear” in the line-of-sight velocity, which is consistent with infalling material onto the principle axis of the Virgo Cluster. CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 15

2.6.4 NGC 4472 (M 49)

M 49 is the brightest member of the Virgo Cluster and is thought to host roughly 6000 GCs [Côté et al., 2003]. A total of 263 GCs have been observed using MOS on CFHT by Zepf et al. [2000] and using the Low Resolution Imaging Spectrometer on Keck I and II by Côté et al. [2003]. The velocity dispersion of metal-poor clusters is 356±25 km/s and is 221±22 km/s for the metal-rich clusters [Côté et al., 2003]. The global velocity dispersion is 312 km/s [Côté et al., 2003]. The metal-rich population of 105 GCs has no significant rotation (15 km/s) while the metal-poor population of 158 GCs exhibit slight rotation (∼90-100 km/s) [Zepf et al., 2000, Côté et al., 2003]. The system as a whole has a net rotation of 53 km/s, which is more or less entirely due to the metal-poor GCs. The blue and red populations are both rotating along the photometric minor axis, but the red population may be rotating in the opposite direction to the metal-poor clusters [Côté et al., 2003]. Côté et al. [2003] also observed a group of 10 metal-rich GCs oriented along the poles of the major axis, which were rotating along the minor axis. These are thought to be a remnant of a past merger or interaction. GC dynamics, independent of X-ray observations, require that M 49 host a massive dark matter halo. The shape of the velocity ellipsoid is consistent with having a nearly perfect isotropic potential when considering the whole of the GC population [Côté et al., 2003]. The X-ray mass distribution agrees with the GC kinematics [Zepf et al., 2000]. If the galaxy formed through mergers, then angular momentum trans- fer must have been extremely efficient to observe such a small rotation. Simulations are able to reproduce this when excluding dark matter halos only. Determining any CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 16

anisotropy between the blue and red populations is diﬃcult due to the large uncertainties associated with the spatial coverage, as well as the large uncertainties of the metal-rich GCs.

2.6.5 NGC 3379

Pierce et al. [2006a] used Gemini/GMOS to measure 22 GC velocities in the Leo Group elliptical, NGC 3379. NGC 3379 is an excellent observational target because it has the advantage of being close enough to obtain precise GC velocity, abundance and age information, and for HST to observe resolved stellar populations. The projected velocity dispersion is found to be roughly constant with increasing radius, indicating the presence of a dark halo. Pierce et al. [2006a] compare their results to the PNe observations of Romanowsky et al. [2003], which demonstrated that the PNe velocity dispersion profile decreased with radius. Pierce et al. [2006a] state that a moderately sized dark matter halo is required, contrary to the minimal dark halo required by Romanowsky et al. [2003]. It is noted that at smaller radii the velocity dispersions of both GCs and PNes agree. As radius increases, the PNe dispersion decreases. They explain this discrepancy by suggesting that the tracer dynamics are different where the PNe trace light, whereas GCs trace mass (see Section 2.4.1). Both samples are small and either may be affected more by small number statistics. Other observations of X-ray gas and HI regions both indicate the presence of a dark halo. CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 17

2.6.6 Overall Observations of GCSs

It has been observed in galaxies NGC 5128, M 87, NGC 1399, NGC 3379, and NGC 4472, that the velocity dispersion of the GCs is either constant, or increasing with radius. The optical and X-ray observations both agree that each of these galaxies is embedded in a massive dark matter halo [Bridges et al., 2006]. Merger simulations indicate that the GCs should have large angular momentum at large radii however this is only observed in some systems [Vitvitska et al., 2002]. The galaxies NGC 1399, NGC 3379 and NGC 4472 exhibit little rotation. Both NGC 3379 and NGC 4472 have large sample sizes, therefore this lack of rotation should not be caused by small sample sizes [Bridges et al., 2006]. For data with sufficient information, the GC population has been divided into blue and red populations to show that their dynamics, rotation and dispersion are different. The metal-rich population is typically dynamically hotter and has a shallower spatial distribution than their metal-poor counterparts (for example: NGC 5128, NGC 1399, NGC 4472) [Bridges et al., 2006]. In few cases the populations are counterrotating as is the case for M87 and NGC 4472. Very few studies have had sufficient data to make any conclusions on the anisotropy,

2 2 β ≈ 1−vθ /vr , of the GCS [Brodie & Strader, 2006]. NGC 1399, NGC 4462 and M87 have been examined for orbital anisotropy of the GCS, however, these galaxies are consistent with having isotropic orbits. Numerous simulations by Dekel et al. [2005], Diemand et al. [2005], Abadi et al. [2006] have suggested that there is approximate isotropy within the central regions of a galaxy, and the radial anisotropy increases with increasing distance up to the half-mass radius, where β ∼ 0.5 for the tracer CHAPTER 2. BACKGROUND ON GLOBULAR CLUSTER SYSTEMS 18

population [Brodie & Strader, 2006]. In order to gain more information about the anisotropy, larger datasets of size of roughly 500-1000 GCs are required [Brodie & Strader, 2006]. Chapter 3

NGC 720

3.1 Overview of Properties

NGC 720 is an isolated, E5 type elliptical galaxy. It has 6 nearby faint companions (3 magnitudes fainter) within a 1.5 square degree ﬁeld, which caused it to be categorized as a galaxy group initially. [Dressler et al., 1986, Humphrey et al., 2010]. The faintness of these companions in relation to NGC 720 indicate that NGC 720 may be a fossil group [Brough et al., 2006]. A fossil group is a galaxy group that has undergone mergers and is thought to be in the last stages of evolution. The group is comprised of a dominant, luminous, central galaxy with an extended X-ray halo and several lower luminosity companions [Mendes de Oliveira & Carrasco , 2007]. It is a relatively well studied galaxy, especially recently at X-ray wavelengths. A few properties are listed in Table 3.1. We adopt a distance of 25.7 Mpc for NGC 720 [Tonry et al., 2001, Jensen et al., 2003].

19 CHAPTER 3. NGC 720 20

Roughly 60 GCS have been studied so far; however, most of these systems reside in galaxy clusters such as Virgo, Coma, Perseus, Hydra, and Fornax [Kissler-Patig et al., 1996]. Very few studies have examined the GCS residing in ﬁeld galaxies. Both Kissler-Patig et al. [1996] and Buote et al. [2002] selected NGC 720 as a target, because it was an elongated and bright ﬁeld galaxy. The selection of NGC 720 for this study has an additional reason; numerous, detailed X-ray studies of NGC 720 have been conducted in recent years. The X-ray emission and GC distributions overlap, therefore our study will allow a comparison to be made between them. This comparison will provide valuable information about the gravitational potential of NGC 720.

Table 3.1: Properties of NGC 720

Property Value Reference MV −21.18 ± 0.25 mag Kissler-Patig et al. [1996] Speciﬁc Frequency 2.2 ± 0.9 Kissler-Patig et al. [1996] Expected # of GCs 660 ± 190 Kissler-Patig et al. [1996] Scale Radius 3.1 kpc Humphrey et al. [2006] Distance 25.7 Mpc Tonry et al. [2001] Jensen et al. [2003] 10 LB 3.1 × 10 L Humphrey & Buote [2006] −1 VT0 3.11 × 10 Jy RC3 catalogue

VT0 10.17 mag RC3 catalogue 14 F at VT0 5.42 × 10 Hz RC3 catalogue 10 LV 4.96 × 10 L Calculated from RC3 catalogue 11 LK 1.7 × 10 L Humphrey et al. [2006] 40 LX (2.95 ± 0.12) × 10 ergs/s Humphrey et al. [2010] CHAPTER 3. NGC 720 21

3.1.1 Optical Studies

The only previous study of the GCs of NGC 720 is a photometric study conducted by Kissler-Patig et al. [1996]. They ﬁt the angular GC distribution to a double sinusoidal curve with two peaks separated by ≈ 180◦, and determined that the GC

◦ ◦ position angle (θ0) is 147 ± 10 . This position angle is consistent with both the

◦ ◦ photometric results of Goudfrooij et al. [1994], where θ0= 141.6 ±0.2 , and with the

◦ surface brightness results of Peletier et al. [1990], where θ0 = 141.7 . Kissler-Patig et al. [1996] measured the position angle of the galaxy light at 142◦ ± 3◦. Kissler-Patig et al. [1996] measured an ellipticity of 0.5±0.1, which is slightly larger than the value of Goudfrooij et al. [1994], with = 0.43 ± 0.01, and of Peletier et al. [1990], with = 0.40. The ellipticity of the galactic light between a semi-major axis of 15-150” is 0.45 ± 0.05. While the ellipticity, position angle and surface density profile of the GCS agree with the galactic light profile, they both do not agree with the position angle or ellipticity of the X-ray isophotes [Buote & Canizares, 1994, Kissler-Patig et al., 1996]. The GC density profile, fitting for 500 objects down to the GC luminosity function turnover (GCLF) magnitude at V= 23.95 ± 0.15 mag, was used to calculate the surface brightness profile. A power law was fit of form Σ ∼ r−x to the surface brightness profile, for which x=2.2 ± 0.1. The semi-major axis is r, Σ is the surface density or light flux and x is the slope of the profile. We used this GC density profile to determine γ in Section 5, Figure 5.10 for our tracer mass calculations. The GCLF turnoff magnitude was translated to a distance modulus, (m-M) = 31.35 ± 0.35 mag, or a distance of 18.6 ± 2.2 Mpc. CHAPTER 3. NGC 720 22

Rembold et al. [2005] investigated the stellar population of NGC 720 using longslit optical spectroscopy out to a radius of 1.64 kpc and measured a strong age gradient along the semi-major axis. The flux in the centre of the galaxy is dominated by a stellar population of age 13 Gyr with solar metallicity. At a galactocentric radius of 0.73 kpc the flux is instead dominated by a stellar population of age 5 Gyr with solar metallicity. As the radius increases from 1 kpc an increasing fraction of the flux is dominated by a stellar population of age 2.5 Gyr. The gradient in the Mg2 abundance suggests that a merger event may have occurred in the past. Rembold et al. [2005] propose that NGC 720 underwent a merger event ∼ 4 Gyr ago based on the J-band brightness profile decomposition and the spectral synthesis. In mergers between disk galaxies with mass ratios 4:1-10:1 the resulting galaxy forms a hybrid system with two parameters: a radial profile consistent with having a massive expontential disk component and a central bulge component, and a velocity dispersion equal to or greater than the rotational velocity [Bournaud et al., 2004]. They find evidence for a bulge-like, small-scale, massive, old spheriod between the ages of 13-5 Gyr and a large-scale disk component of age 5-2.5 Gyr. The properties of NGC 720 fit both these qualities, supporting an evolutionary history that included an unequal-mass merger no less than 4 Gyr ago; it takes roughly 2 Gyr for symmetries to dissipate and another 2 Gyr for the system to relax [Rembold et al., 2005]. They compute the stellar rotational velocity as >50 km/s. The velocity dispersion is σ = 250 km/s in the central region which decreases to σ = 205 km/s at 3.29 kpc. They estimate a

11 total stellar mass of 3.29 × 10 M [Rembold et al., 2005]. CHAPTER 3. NGC 720 23

3.2 X-ray Observations

NGC 720 is a strong X-ray source and the NGC 720 galaxy group has extended intra-group X-ray emission [Rembold et al., 2005, Brough et al., 2006]. The X-ray emission can be measured out to a radius ∼ 80kpc [Humphrey et al., 2010]. The morphology of the X-ray emission is relaxed, suggesting that a reliable measurement of the gravitating mass can be obtained. X-ray emission from hot gas provides accurate information about the gravitational potential about a galaxy out to far radii [Buote & Canizares, 1994, Humphrey et al., 2006]. Assuming spherical symmetry and using the equation for hydrostatic equilibrium and the ideal gas law we can obtain a mass via equation 3.1

rk T (r) dlnρ dlnT M(< r) = − B gas gas + gas (3.1) Gµmp dlnr dlnr

where Tgas is the temperature of the gas, ρgas is the density of the gas, G is New- ton’s constant, kB is the Boltzmann constant, µ is the mean atomic weight of the gas and mp is the proton mass. The assumption of the gas being in hydrostatic equilibrium is valid for several reasons, which are discussed in depth in Buote et al. [2002]. A few of these reasons include: the X-ray isophotes have no other irregular features/assymmetries apart from the twist [Humphrey et al., 2010], the position angle and ellipticity calculations independently support the need for dark matter, and NGC 720 is isolated, therefore the diﬀuse gas should not be perturbed by external inﬂuences. Since Terlevich & Forbes [2002] and Rembold et al. [2005] determined CHAPTER 3. NGC 720 24

that NGC 720 had undergone a gas-rich merger with a neighbor 3-4 Gyr ago, suf- ﬁcient time has elapsed for the system to relax. This is supported by Lauer et al. [1995] classifying NGC 720 as a core galaxy with rounded isophotes.

NGC 720 is therefore at least in hydrostatic equilibrium within a . 150” [Buote & Canizares, 1994, Buote et al., 2002]. For a & 150” the PA twist cannot be explained by a single triaxial model in hydrostatic equilibrium, indicating a more recent merger may have occurred. Jeltema et al. [2003] observed an ”arc” of X-ray point sources at a ∼ 120” which may be a piece of a shell or other merger remnant, supporting this idea. No arcs in optical wavelengths have been detected, however, any arcs would

−1 have dissipated after 1.4h70 Gyr of the merger took place [Rembold et al., 2005]. Using the Chandra and Suzuku observatories, Humphrey et al. [2010] obtained deep X-ray measurements of NGC 720. From these data Humphrey et al. [2010] infer that there is little nonthermal pressure using a Kroupa IMF and single-burst

SSP models. The SSP models predict a M/LK = 0.35 ± 0.07, but they measured

M/LK = 0.54 ± 0.05. This higher ratio can be explained by the presence of a younger stellar population (∼ 3 Gyr), dominating the luminosity and overlying an older population, which has a higher M/L ratio [Humphrey & Buote, 2006, Rembold et al., 2005, Humphrey et al., 2010]. The younger ∼ 3 Gyr stellar population is further evidence indicating that NGC 720 is likely a result of a recent merger. This

M/LK is not representative of the whole system [Humphrey et al., 2010]. Varying the stellar M/L by ∼ 20% can produce variations in Mvir of 50 − 100% [Humphrey et al., 2006]. CHAPTER 3. NGC 720 25

3.3 The Isophotes of NGC 720

The shape of isophotes can reveal information about the intrinsic structure, including triaxiality, in ellipticals [Rembold et al., 2002]. NGC 720 was classified by Nieto et al. [1994] as having ”irregular isophotes”, being neither boxy, nor disky, indicating that the galaxy may have undergone a merger or interaction. X-ray observations of NGC 720 support the presence of a twist in the isophotes (Nieto & Bender 1989). An isophote twist is produced when the projected triaxial light distribution varies with radius [Romanowsky & Kochanek, 1998]. The ellipticity and position angle of the optical isophotes do not agree with the twisted X-ray isophotes [Buote & Canizares, 1994, Kissler-Patig et al., 1996]. The infrared isophotes have a position angle consistent with the optical values [Rembold et al., 2002]. Buote et al. [2002] use Chandra to examine the dark matter halo of NGC 720. The semi-major axis within a . 150” has an ellipticity x ∼ 0.15, which is significantly lower than previous ROSAT estimates of 0.2-0.3 at 90% confidence for a < 100” [Buote & Canizares, 1994]. Buote et al. [2002] confirm the twist in position angle observed in the ROSAT data, however, the nature of the twist is different. In addition to being shaped differently, the optical and X-ray isophotes are twisted by ∼ 30◦ ±15◦ [Buote et al., 2002]. The Chandra data reveals that the position angle twist for a ∼ 60” gradually increases from 120◦ to 135◦ as a decreases towards the centre.

◦ The position angle between 80” . a . 150” is 110 . For semi major axes between 150”-185”, the position angle and ellipticity diverge from their values at smaller semi-major axes. The discrepancy between the X-ray isophotes and the GC/galaxy light isophotes is consistent with the presence of a triaxial dark matter halo [Buote CHAPTER 3. NGC 720 26

et al., 2002]. The Romanowsky & Kochanek [1998] triaxial mass model, using the Chandra data, is able to explain the position angle twist. Considering only the ellipticity, a massive dark matter halo is required, because the observed ellipticities are too large for the mass to gravitationally trace the optical light, regardless of any position angle twist, X-ray gas temperature proﬁle, or pressure [Buote & Canizares, 1994, Buote et al., 2002]. Therefore, using purely geometrical reasoning, an extended dark matter halo is required with ∼ 19.6σ signiﬁcance [Buote & Canizares, 1994, Buote et al., 2002, Humphrey et al., 2010].

3.4 Previous Determinations of the Mass of NGC

720

Buote & Canizares [1994] estimate the total mass within a semi-major axis ellipsoid

12 ( ∼ 0.50 − 0.70) with radius 43.8h80 kpc as (0.41 − 1.4) × 10 h80M . They ﬁnd no signiﬁcant temperature gradient or anisotropies. Brough et al. [2006] measure the

+0.4 12 virial mass within r500 = 0.40 Mpc as (3.1−0.3)×10 M using the velocity dispersion (117 ± 54 km/s) of its companion dwarf galaxies and X-ray modelling. This small virial mass is only a few times larger than that of the Milky Way, but the value

+2.4 12 agrees with a previous measurement by Humphrey et al. [2006] of (6.6−3.0) × 10 M

+50 measured within r = 480−90 kpc for a NFW+stars model. Virial quantities are very diﬃcult to measure [Humphrey et al., 2010]. For example, Gastaldello et al. [2007] show that accurate measurements are heavily dependent on the scale radius. A small CHAPTER 3. NGC 720 27

scale radius implies that the dark matter halo must be extremely concentrated. The

+30 isolated nature of NGC 720 may explain this high concentration (18−7 ), as it must have formed at an early epoch out of several bright subhalos; their merger could be expected to increase the dark matter concentration. The concentration can decrease if there is more stellar mass at the centre of the galaxy [Humphrey et al., 2006]. The concentration is the ratio of the virial radius and the virial mass [Humphrey et al., 2006]. Humphrey et al. [2010] measure the mass within fb,2500 = 0.10 ± 0.1

12 as Mf,2500 = (1.6 ± 0.2) × 10 M with systematic errors of roughly 20%. Within a radius of ∼ 30 kpc they ﬁnd the total gravitating mass proﬁle distribution that has a shape close to a singular isothermal sphere. Chapter 4

Data Reduction

4.1 The Data

The data for NGC 720 were obtained over a span of 3 years from 2007 to 2010 using the 8 m Gemini Multi-Object Spectrograph (GMOS) at the Gemini South telescope in Cerro Pachon, Chile. Observing time was obtained in 2007B, 2008B and 2009B. The data from 2007B and 2008B were obtained before I began my Master’s program. For the 2009B data, however, I helped prepare the proposal and the Phase II observations. The data were entirely reduced by me.

4.1.1 Semester 2007B: Gemini South

• Principle Investigator: Ray Sharples

• Co-Investigators: Mike Beasley, Terry Bridges, Favio Faifer, Duncan Forbes, Juan Forte, Karl Gebhardt, Dave Hanes, Mark Norris, Steve Zepf

28 CHAPTER 4. DATA REDUCTION 29

The research team were awarded 22.29 hours to observe NGC 720 for the purpose of extending their environmental survey of GCSs into more isolated environments. The GCSs of NGC 3923, NGC 3379, IC 1459, and NGC 4649 had previously been studied. The aim was to obtain spectroscopy for a sample of ∼ 120 GC candidates, of which 90-100 were expected to be confirmed GCs. One deep field and two moderately deep fields were observed. The first Field spanned the centre of NGC 720 with the second field offset to the NW and third field offset to the SE of Field 1. Fields 2 and 3 overlap Field 1 along the major axis (see Figure 4.1). Table 4.1 lists the field positions. Pre-imaging for each field was necessary to determine the locations of GC candidates, in order to perform the follow up spectroscopy. In addition to providing GC identification, the pre-imaging was also used for performing aperture photometry to extract magnitude and colour information. Investigation of the colour distribution required the photometry to reach 0.5 mag fainter than the GC turn-over magnitude for NGC 720 at V = 24.5 mag [Kissler-Patig et al., 1996]. The GMOS Integration Time Calculator (ITC), available on the Gemini website, was used to calculate this. The ITC used a G2V stellar template to determine that a 70%-ile image quality, 2x2 binning and 4x120 sec exposures were necessary to obtain a S/N ∼ 5 . Images were taken with the g’ (g G0325 filter), r’ (r G0326 filter), and i’-bands (i G0327 filter). When performing the spectroscopy, the B600 grating was dithered across two spectral wavelength configurations (at 500 nm and at 505 nm) to cover the 37 pixel gap between the CCD chips. Such a setup had a spectroscopic dispersion of 0.45 A/pix˚ and a corresponding velocity resolution of 30 km/s/pix. For a 1.0” wide CHAPTER 4. DATA REDUCTION 30

slit, the slit width used for the spectroscopy, this setup has a FWHM resolution of roughly 4.8A˚. The bandpass was restricted to wavelengths of 4600-5250A˚ using the Sloan g (G0301) and the GG455 (G0305) ﬁlters. This wavelength region contains

Hβ at 4861A˚, Mgb at 5177A˚, Fe5270, and Fe5335. Using this bandpass had the dual purpose of truncating the spectra in regions the team was not interested in, and allowed more slits to be placed on the MOS mask. Accurate velocity measurements required a S/N ∼ 5/A˚. This resolution required, using the ITC with a G2V stellar template, a 70%-ile image quality, 4x2 (spatial x spectral) binning, and a 4 h exposure time to reach V = -22.5 at 450 nm. The study was not limited by water vapour, but it did require a sky background of 50% and less than 50% cloud cover. These conditions were met during all observations. For each spectroscopic observation a 2 sec GCAL flat image, a 20 sec arc using a copper-argon lamp and 2x1800 sec exposures of the field were taken. This process was repeated for the duration of the observation run. The spectroscopy of the third field was weathered out, therefore this field was resubmitted for the 2008B semester.

4.1.2 Semester 2008B: Gemini South

• Principle Investigator: Terry Bridges

• Co-Investigators: Mike Beasley, Favio Faifer, Duncan Forbes, Juan Forte, Karl Gebhardt, Dave Hanes, Mark Norris, Ray Sharples, Steve Zepf

With data from the past year it was possible to extend the sample size up to 200 GCs where 160-180 could be expected to be genuine GCs. This proposal included CHAPTER 4. DATA REDUCTION 31

additional observations of two more fields: Fields 4 and 5 are offset from Field 1 along the minor axis to the NE and SW (see Figure 4.1 and Table 4.1). These new fields required pre-imaging. The research team was awarded 15.3 hours to continue observations, however no data, except for the pre-imaging of Fields 4 and 5, were obtained.

4.1.3 Semester 2009B: Gemini South

• Principle Investigator: Dave Hanes

• Co-Investigators: Terry Bridges, Favio Faifer, Duncan Forbes, Juan Forte, Karl Gebhardt, Mark Norris, Amanda Schembri, Ray Sharples, Steve Zepf

We were awarded 13.8 hours to complete the spectroscopy that was unﬁnished during the 2007B and 2008B semesters. The spectroscopy was carried out in the same man- ner as in the 2007B semesters with the same observing conditions. These conditions were met for all observations. All data were obtained for all ﬁelds.

Table 4.1: Location of the ﬁelds used for spectroscopy of NGC 720

Field RA Dec 1 01:53:00.3 -13:44:17.0 2 01:53:07.0 -13.46:11.0 3 01:52:54.3 -13:44:18.0 4 01:53:05.94 -13:43:05.83 5 01:52:54.36 -13:45:15.26 CHAPTER 4. DATA REDUCTION 32

Figure 4.1: The ﬁve ﬁelds for NGC 720. Field 1 is the center green box, Field 2 is the green box to the NW, Field 3 is the blue box to the SE, Field 4 is red box to the NE and Field 5 is the red box to the SW. The RA coordinate increases to the left. CHAPTER 4. DATA REDUCTION 33

4.2 The Gemini Multi-Object Spectrograph

The GMOS is a multi-object spectrograph used at both Gemini North and Gemini South since late 2001. GMOS has four operation modes: imaging, long-slit spectroscopy, multi-object spectroscopy and integral field unit spectroscopy. The CCD chips consist of a 3x1 EEV array with pixel dimensions of 6144x4608 pix and a field of view of 5.5x5.5 arcmin. There is are two 39 pixel gaps between the CCD chips, corresponding to a 2.8 arcsec gap. It is recommended to dither using two central wavelength configurations to obtain observations spanning the chip gap. The imaging field of view is 330x300 square arcsec. The spectrograph is sensitive for a wavelength range of 0.35 − 1.1µm [Hook et al., 2004]. Guiding, tilt-tip operations, and astigmatism signals are corrected real time using a 2x2 lenslet array by the on-instrument wave-front sensor (OIWFS). The OIWFS is mounted on a movable arm that is mounted in front of the mask plane. The OIWFS is also used in MOS operations to ensure that the mask correctly aligned with the acquisition object positions [Hook et al., 2004]. MOS spectroscopy uses custom laser milled masks with a minimum slit width of 0.5”. Roughly 60 slits can be placed on one mask, with a maximum of 100 slits if narrow-band filters are used. The mask designs are optimally based off of previous Gemini imaging.

4.3 The Reduction

Data reduction was performed using Gemini IRAF version 2.14.1. The data of NGC 720 uses two spectral wavelength settings in order to dither across the chip gaps in CHAPTER 4. DATA REDUCTION 34

the Gemini GMOS CCD camera. One configuration is centered at 500 nm and the other at 505 nm. These two pointings were reduced separately for all fields. Included below is the reduction process used to extract the spectra. For clarity, sample file names will be included: S500gcal.fits, and S505gcal.fits are the GCAL flat fields for the pointings at 500 nm and 505 nm. S500sci1.fits, S500sci2.fits, S505sci1.fits, S505sci2.fits are the science frames. S500arc.fits, S505arc.fits are the CuAr lamp arcs. The bias frame we will call bias.fits. In all cases the default parameters, unless specified otherwise.

1. gsflat S500gcal.fits rgS500gcal flat.fits order=29 bias=bias.fits fl usegrad+ >>> combflat=rgS500gcal comb.fits fl keep+ The gsflat task creates a normalized spectroscopic flat field. Flat fielding correction removes differing gains and dark currents to obtain the pixel-to-pixel variations in the CCD chip. It will remove the GCAL lamp and GMOS spectral response, but will not fix any fringing that occurs. The output flat field is called rgS500gcal flat.fits. The order specifies the number of pieces in the spline used for fitting the response function. The fl keep+ parameter keeps the imcombined flat field, which is needed in the following step. The combflat parameter specifies the name of the combined flat field. The fl usegrad parameter uses a gradient method to find the edges of the MOS slits.

2. gscut rgS500gcal comb.fits fl update+ gradimage=rgS500gcal comb The gscut task cuts the MOS slits into 2D spectra. We use the gradient image, the comb file, to identify the slit edges, because it preserves the slit locations in the MDF (Mask Definition File). The fl update+ parameter saves the new CHAPTER 4. DATA REDUCTION 35

slit locations to the MDF.

3. ttools>tedit the MDF slit positions The slit locations gscut identified are not always correct. Approximately half the slits were identified improperly, which, if not fixed, caused the spectra to overlap for subsequent reductions. To fix this we had to display the gradient file in ds9 and with the cursor manually confirm that the location of each slit was correct in the MDF. Usually the dispersion axis (x-axis) was correct, but the spatial axis (y-axis) could be off by 2 to 50 pixels at times. Due to the nature of multi-object spectroscopy some of the spectra overlapped in the spectral direction. When this occurred the MDF was altered to truncate the overlapping sections. Once the offsets were determined in the previous step, the MDF was edited using ttools. This step must be repeated for all GCAL flat fields.

4. gemcombine all flat files Combine all the flats together into one file using a median combining method and no pixel rejection.

5. gemcombine all comb files Combine all the comb files together into one file using a median combining method and no pixel rejection.

6. gprepare science frames The gprepare task prepares the images for further reduction. This ensures that signal-to-noise is preserved, that the MDF is associated with the science frames, CHAPTER 4. DATA REDUCTION 36

and that it attaches header information required for further reductions. The fl addm parameter must be turned oﬀ to work properly.

7. gprepare arc frames Run the gprepare task again, but for the arc frames.

8. gemcombine the gprepare-d science frames Run gemcombine to combine the four science frames together using a median combine and no pixel rejection. We rely on the median combining and pixel thresholds to eliminate most cosmic rays or other problem pixels.

9. gemcombine gprepare-d arc frames using a maximum of 2 arc frames Run the gemcombine task to combine a maximum of two arc frames together, again using a median combine and no pixel rejection. We found that using more than two frames resulted in broad emission peaks that caused problems for the gswavelength task. Our data consisted of two arcs per night per central wavelength. Usually only one pointing of a central wavelength was observed per night. Therefore the arcs that were chosen to be combined were taken from diﬀerent nights of observation.

10. gsreduce combined science frame Run the gsreduce task on the combined science frame. We specify the flat field, the bias frame, and the refimage (the combined comb image) whose MDF provides the slit information to the task, so that each slit may have its own .fits extension. Cosmic ray rejetion is turned off. CHAPTER 4. DATA REDUCTION 37

11. gsreduce combined arc frame Run the gsreduce task on the combined arc frame. We turn off flat fielding and dark frame subtraction. The bias is subtrated and the comb refimage is used for its MDF information.

12. gswavelength arc frame The arc is now ready to be wavelength calibrated. We run gswavelength using step=2. The step=2 parameter indicates that the spatial direction is calibrated every 2 pixels. If a larger step is used, the lamp lines become distorted. Within the task an order > 4 for the number of coefficients chebyshev polynomial fitting function was usually required. It must be run interactively, and must be run to completion before the results are saved to the image. If mistakes are made, the task quits and one must begin again. The fields contained roughly 50 slits per frame. Ultimately, wavelength calibration ensures that if there are any spectral shifts in either the spectral or spatial direction within a slit, then they are tracked. Most slits are 10-20 pixels in height, yielding a work time of roughly 24 hours for completion. We use a copper-argon calibration lamp and its corresponding line index list provided by the Gemini IRAF package. We begin by manually specifying the eight major emission peaks located at 4657.9012A˚, 4726.8683A˚, 4764.8646A˚, 4806.0205A˚, 4847.8095A˚, 5017.1628A˚, 5187.7462A˚, and 5495.8738A˚. These lines are well distributed across the spectral range of 4500-5500A˚ to ensure an even calibration. These emission peaks are shown in Figure 4.2. CHAPTER 4. DATA REDUCTION 38

Figure 4.2: A sample arc calibration where the initial 8 emission lines are identiﬁed. These lines are identiﬁed by the user and then the gswavelength task uses these to autoidentify the rest. The x-axis is the wavelength in angstroms and the y-axis is in counts. CHAPTER 4. DATA REDUCTION 39

Manually identifying these lines ensures that when we refit (keystroke ’l’) af- terwards then the task uses the index list to identify the other peaks relative to the peaks manually identified. Using the ’f’ keystroke we refit the peak positions, viewing the residuals of the fitted to the user coordinates. Using the RMS in this fitting screen we delete outlying peak positions manually to minimize the RMS. Typically we want an RMS < 0.08 which includes no less than 16 data points. Typically we were able to obtain an RMS of roughly 0.075 using 20 points. We would also ensure that the points selected corresponded to peaks which spanned the whole wavelength range so as to provide an accurate calibration across the whole spectral direction. Points would be deleted, the residuals would be refitted and this process would be repeated until a sufficient RMS was met.

13. gstransform arc frame Once the necessary wavelength calibration was determined in the previous step we ran gstransform to apply the calibration to the arc frame

14. gstransform science frame We then use the calibrated arc frame to apply the calibration to the science spectra.

15. imexamine transformed science frame to check for sky line 5577A˚ To ensure that the calibration was properly applied we check each spectra for the night sky line at 5577A˚.

16. gsskysub science frame CHAPTER 4. DATA REDUCTION 40

The task gsskysub subtracts any contribution from the sky. The parameter, mos sample = 0.8, determines the maximum fraction in the spectral-direction of the slit that can be used to identify they sky.

17. gsextract science frame The spectra are now fully reduced and the spectra are extracted using the task gsextract. Set tnsum = 100 and tstep=50. The tnsum parameter is the number of dispersion lines counted at each tstep when tracing the spectra along the dispersion-axis. The tstep parameter is the number of steps along the dispersion-axis, when tracing spectra, between the determination of the spectrum positions.

To improve the signal-to-noise we now need to combine the 500 nm and 505 nm conﬁgurations. This is done using the following procedure:

1. scombine each 500 nm extension to its corresponding 505 nm extension Use the default parameters except for a median combine method, and an avisigclip rejection method. We select a sample region bounding 500 nm to 525 nm. The avisigclip rejection is an averaged sigma clipping algorithm, which assumes that at each point the sigma about the median is proportional to the square root of the median. It rejects pixels outside of the threshold values using an iteration method. In order to combine the spectra, the scombine task must interpolate the spectra (if necessary) to a common dispersion sampling. The sample region is the dispersion coordinates which are used to calculate spectrum statistics used in scaling and weighting. This bounded region was chosen CHAPTER 4. DATA REDUCTION 41

because it is relatively featureless. This task was run for each science (spectra) extension.

2. scopy the combined spectra extensions into one multispec file We then created a multispec file using the task scopy. Default parameters used except for turning renumbering and rebining on. We now have a final file per field containing all fully reduced spectra.

4.4 The Spectra

The S/N of all spectra were measured between 5000-5025A˚ using the splot task (the ’m’ keystroke) in IRAF. This region of the spectrum has the most signal, but few features. There is some uncertainty in the calculated S/N values because the S/N is very sensitive to the location of the cursor. This is still a fair measure of how the S/N compare for all spectra in the same region. The object with S/N ∼ 64 is not a globular cluster based on the spectrum. In Figures 4.3, 4.4, and 4.5 we show a few examples of conﬁrmed GCs with low, medium and high S/N respectively. Figure 4.6 shows two samples of more unusual spectra, of which the one on the left is a background galaxy. Figure 4.7 shows the spectra of the center of NGC 720.

4.5 Astrometry

In Chapter 3 it was pointed out that little kinematics work has been done for NGC 720. There is no GC astrometry to refer to, therefore we use the USNO-B1.0 Catalog CHAPTER 4. DATA REDUCTION 42

Figure 4.3: Examples of conﬁrmed GCs with low S/N. The spectrum on the left has a S/N = 2.9 and on the right, S/N =3.5.

Figure 4.4: Examples of confirmed GCs with medium S/N. The spectrum on the left has a S/N = 13.0, and on the right, S/N = 11.8. In both figures, Hβ is visible at 486.1 nm and the Mg B lines are distinctly visible at 517.2 nm and 518.3 nm only for the figure on the right. CHAPTER 4. DATA REDUCTION 43

Figure 4.5: Examples of conﬁrmed GCs with high S/N. The spectrum on the left has a S/N = 15.5, and on the right, S/N = 28.8. Hβ is visible at 486.1 nm and the Mg B lines are visible at 517.2 nm and 518.3 nm.

Figure 4.6: Examples of unusual spectra. The spectrum on the left has S/N = 14.1, and on the right, S/N =64.5. CHAPTER 4. DATA REDUCTION 44

Figure 4.7: Spectrum of the center of NGC 720. to perform the GC astrometry [Monet et al., 2003]. We perform the search using the Integrated Image and Catalogue Archive Service using the following parameters (http://www.nofs.navy.mil/data/FchPix/):

• RA = 01:53:01.4

• DEC = -13:43:59.0

• EPOCH = 2000.0

• Box width for both RA and DEC = 0.1666667 degrees

• Catalogue = USBO B1.0

• Image Data = All Surveys CHAPTER 4. DATA REDUCTION 45

• Faint Magnitude Limit = 100.0

The search engine found 228 objects which fit the criteria and returned the RA and Dec, B, R and I-band magnitude information. Using this table of data we then manually compared object positions from the table with images of NGC 720 for each of the five fields. The RA and Dec of the object in our images were read off the ds9 window and recorded. Our goal was to locate roughly 20 objects in each field. We made sure to locate objects which covered the whole span of the image field in order to obtain a good transformation between the known locations and the observed locations in our images. The matches are shown in Figures 4.8, 4.9, 4.10, 4.11 and 4.12, where object candidates are marked by blue circles, astrometry matches are marked by green circles and acquisition stars are marked by red circles. Once the positions of the image objects have been matched to objects in the catalogue, we used the images>immatch>geomap task in IRAF to determine the necessary transformation between the image positions using the catalogue as the template. The geomap task parameters were all set to default settings except for fitgeom = rxyscale. The fitgeom = rxyscale setting calculates a transformation that allows for x and y shifts, x and y magnification factors, and rotation angle shifts. Geomap outputs the reference mean for RA and DEC, the image mean for RA and DEC, the geometry used (rxyscale), the fitting function used (a polynomial), the x and y shift, the x and y magnitude, the x and y rotation, and the RMS in the x and y directions. The rxyscale fitting parameters are used to calculate the coefficients b, c, e and f which are defined in equations 4.4, 4.5, 4.6, 4.7, 4.8 and 4.9. First, we CHAPTER 4. DATA REDUCTION 46

Figure 4.8: Field 1 Image in g-band. 18 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering. CHAPTER 4. DATA REDUCTION 47

Figure 4.9: Field 2 Image in g-band. 18 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering. CHAPTER 4. DATA REDUCTION 48

Figure 4.10: Field 3 Image in g-band. 22 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering. CHAPTER 4. DATA REDUCTION 49

Figure 4.11: Field 4 Image in g-band. 15 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering. CHAPTER 4. DATA REDUCTION 50

Figure 4.12: Field 5 Image in g-band. 15 matches were found. Blue circles mark candidate objects. Green circles mark matches between the USNO B1.0 catalogue and the image. Red circles are acquisition stars used for mask centering. The OIWFS arm can be seen on the right side of the image. CHAPTER 4. DATA REDUCTION 51

deﬁne the xrotation and yrotation parameters with equations 4.1 and 4.2:

xrotation = rotation + 0/180 (4.1)

yrotation = rotation (4.2)

const = mag ∗ mag (4.3)

We can now calculate the coeﬃcients b, c, e, f, a and d:

b = xmag ∗ cos(xrotation) (4.4)

c = ymag ∗ sin(yrotation) (4.5)

e = −xmag ∗ sin(xrotation) (4.6)

f = ymag ∗ cos(yrotation) (4.7)

a = xshift = xin0 − b ∗ xref0 − c ∗ yref0 (4.8)

d = yshift = yin0 − e ∗ xref0 − f ∗ yref0 (4.9) where xref0 and yref0 are catalogue coordinates and xin0, yin0 are the input image coordinate. Using these coeﬃcients, we can now apply the transformation using equation 4.10 and 4.11: xin = a + b ∗ xref + c ∗ yref (4.10) CHAPTER 4. DATA REDUCTION 52

yin = d + e ∗ xref + f ∗ yref (4.11) where xref and yref are the original image position (ie: globular cluster candidate coordinates) and xin, yin are the outputted transformed position relative to the catalogue in degrees. The check b ∗ f − c ∗ e = const can be used to ensure correct coefficient calculation. The globular cluster candidate coordinates are taken from the MDF file. Figure 4.13 shows the transformed coordinates of all objects observed, where Field 1 objects are blue, Field 2 are green, Field 3 are red, Field 4 are magenta, Field 5 are cyan and acquisition objects are black. Figure 4.14 shows the same points (filled in markers) as in Figure 4.13, but with the original coordinates marked as hollow symbols. Overall, there was very little shift in the coordinates, and no drastic transformation was required. Figure 4.15 shows the distribution of candidate objects as blue squares. The small magenta triangles are the rejected objects. The spatial distribution of the candidate GCs appears to be flattened. This is not an artifact of the sample being incomplete. Figure 4.16 shows the same data as Figure 4.15 but with colour information included (see discussion is section 4.6).

4.6 Photometry

We extract colour information for the conﬁrmed clusters by performing aperture photometry on the images of NGC 720. For each ﬁeld we have images in Sloan i-, g- and r-bands. Since we are interested in the relative (g-i) colour between the GCs, our study does not require calibration to photometric standards. We do not CHAPTER 4. DATA REDUCTION 53

Figure 4.13: The positions of all objects with the geomap transformation applied. The crosshairs mark the centre of NGC 720. Black circles mark acquisition objects. Blue squares mark Field 1 objects, green triangles mark Field 2 objects, red pentagons mark field 3 objects, magenta triangles mark field 4 objects and cyan squares mark field 5 objects. CHAPTER 4. DATA REDUCTION 54

Figure 4.14: The same figure as Figure 4.13 but with the original untransformed positions marked as hollow shapes. The filled shapes mark the transformed positions as in Figure 4.13. Again, the crosshairs mark the centre of NGC 720. Black circles mark acquisition objects. Blue squares mark Field 1 objects, green triangles mark Field 2 objects, red pentagons mark field 3 objects, magenta triangles mark field 4 objects and cyan squares mark field 5 objects. CHAPTER 4. DATA REDUCTION 55

Figure 4.15: The coordinates of the candidate globular clusters shown as blue squares. Rejected data are shown as magenta triangles. CHAPTER 4. DATA REDUCTION 56

Figure 4.16: The same data as Figure 4.15 but with colour information. Blue cluster candidates are shown as blue squares and red clusters as red pentagons. Again, rejected data are magenta. CHAPTER 4. DATA REDUCTION 57

need the r-band images for this purpose, just the i-band and g-band images. The phot task within the noao>digiphot>apphot package is used to perform aperture photometry. The phot task contains many subtasks which are mentioned below. Only the parameters that have been changed from default are described:

• phot image = F1g.fits[1], coords = F1-GC-physical.txt, output = F1g-photmetry.mag, interative = no, radplot = no, wcsin/out = physical The coords parameter specifies the list of coordinates of confirmed GCs in physical coordinates. The output data was also in physical coordinates. We run phot non-iteratively.

Phot uses the below tasks to accurately calculate the center of all objects speciﬁed in the coordinates list and computes the magnitude and sky values for each object. The magnitude is calculated by equation 4.12, with uncertainty described by equation 4.13. mag = zmag − 2.5 ∗ log 10(ﬂux) + 2.5 ∗ log 10(itime) (4.12)

error merr = 1.0857 (4.13) flux where flux is flux = sum − area ∗ msky (4.14) and error is

s (ﬂux /epadu) + area ∗ stdev2 + area2 ∗ stdev2 error = (4.15) nsky CHAPTER 4. DATA REDUCTION 58

where ﬂux is the total number of counts excluding any sky within the aperture, area is the aperture area in square pixels, mag is the magnitude within the aperture, sum is the total number of counts within aperture including sky, itime is the exposure time deﬁned in datapars, nsky is the number of sky pixels, msky is the best estimate of the sky value per pixel, epadu is the electron gain is adu, zmag (=25.0) is the magnitude zero point, and stdev is the standard deviation in the sky.

• centerpars calgorithm = centroid, cbox = 5.0, maxshift = 10.0 This this the centering task for photometry measurements. We use a centroid algorithm which calculates the intensity-weighted mean of the x and y marginal profiles. The cbox parameter specifies the raster width in units of scale units. The centerpars helpfile suggests using cbox = 2.5-4.0 * FWHM of the PSF. The physical coordinates of GCs were not corrected along with the pixel positions during the reduction process (as discussed in Chapter 4), therefore we use a maxshift of 10.0 scale units to account for all of these shifts.

• datapars scale = 1.0, fwhmpsf = 3.0 scale units, sigma = 9.3 or 870 counts, datamin = 0.00001, datamax = 10000 The parameters of gain, readnoise, xairmass, ifilter, otime, obstime were all copied from the header of the images. The value of sigma is the average of roughly 20 imexamine measurements (’m’ keystroke) of the background sky. The fwhmpsf value is estimated using imexamine again (the ’f’ keystroke) to measure the FWHM of stars/clusters (point sources) from their radial profiles. For stars (objects identified by the astrometry USNO catalogue) I estimated fwhm of roughly 2.5. For candidate clusters they ranged from 2 to 4. I decided CHAPTER 4. DATA REDUCTION 59

to use a fwhmpsf of 3.0 in order to include all of the candidates.

• ﬁndpars threshold = 5.0 This task edits the star detection parameters. The only parameter we altered was the threshold, which is the maximum threshold detection above the background level.

• fitskypars salgorithm = centroid, annulus = 15.0, dannulus = 20.0, skyvalue = 0.0 This task uses a centroid algorithm to fit for the background sky. Annulus and dannulus specify the inner radius and width respectively, of the annulus region to be used in the fitting algorithm. I tested for various values of annulus and dannulus to find the most stable fit: where enough sky was included in the annulus to get an accurate measurement, but not enough to include other nearby stars or other objects.

• photpars weighting = constant, apertures = 15. This task edits the photometry parameters. To ﬁnd the optical aperture size we ranged the apertures parameter from 2 to 40, in steps of 1. We ﬁnd that an aperture of 15.0 is best. At this aperture size, the measured magnitude stopped varying by 0.01.

The testing of phot involved varying the following parameters: centerpars.cbox, centerpars.maxshift, datapars.fwhmpsf, datapars.sigma, datapars.datamin, datapars. datamax, findpars.threshold, fitskypars.annulus, fitskypars.dannulus and photpars. CHAPTER 4. DATA REDUCTION 60

aperture. Some of these parameters were relatively simple to optimize, however oth- ers (cbox, fwhmpsf, annulus, dannulus, aperture) took more work. This testing was done using a systematic trial and error method. Annulus was varied between 5 - 20, dannulus was varied between 10-25, cbox between 2-10 and fwhmpsf between 2-4. The ﬁnal parameter values obtained the most stable magnitudes, ie: the magnitude did not change by more than 0.01. Once the phot task was completed we used pdump to extract the g- and i-band magnitudes. We then subtracted the i-band magnitude from the g-band magnitude as a measure of the relative colour. Each g-band and i-band magnitude had an uncertainty, gerr and ierr respectively, which were averaged. There was little variation between the uncertainties calculated, therefore taking an average is acceptable.

4.6.1 Colours

We now have the colours of the candidate clusters, but need to determine if there is any bimodality in the colour distribution. If we bin the data by choosing arbitrary bin sizes, then the bimodality determined is heavily dependant on the bin size used. To avoid this bias we use a generalized histogram ﬁtting code provided by Karl Gebhardt called ncont1.f [Silverman , 1986, Gebhardt et al., 1996, Gebhardt & Thomas , 2009]. This code uses a smoothing kernel size to estimate the local density using a method called generalized cross-validation. This method essentially minimizes the integrated square error R (f 0 − f)2 where f’ is a density estimate, which is dependent on the smoothing, f. The code iteratively removes one point and recalculates the density until an estimate of f is obtained Gebhardt et al. [1996]. Figure 4.17 shows the CHAPTER 4. DATA REDUCTION 61

results of this ﬁt with the distribution of colours superimposed. The dip between the peaks marks the location of the split at (g-i) = 0.50. The peak of the blue colour distribution is measured at (g-i) = 0.4 and the red peak at (g-i) = 0.6. This bimodal distribution shows that there is a larger population of red GCs than blue GCs.

4.7 Cross-Correlation

We cross-correlate the spectra with templates to determine the velocity of each candidate cluster. Prior to performing cross-correlation, the header of each new multispec ﬁle needs to be edited (hedit task) to include the parameters Epoch = 2000, RA and Dec, UT, Date-Obs, and Exptime. Observations at a particular central wavelength (ie: 500 nm) are usually taken over consecutive nights, but the other central wavelength (ie: 505 nm) is likely observed some days later. We calculate the average UT and Date-Obs for the combined spectra. The maximum amount of time to pass between these sets of observations is at most a month, which is not long enough to cause any discrepancy in any velocity calculations. We set Exptime = 1.0 sec even though the exposure time for each individual spectra (prior to combining) is 1800 sec. The following template headers must also be adjusted: RA = 12.00.00, DEC = 12.00.00, EPOCH = 1993, observatory = KPNO, vhelio = 0.0, exptime = 1.0.

4.7.1 Templates

The templates used for cross-correlation come from MILES (Medium resolution INT Library of Empirical Spectra), an empirical stellar spectral library created CHAPTER 4. DATA REDUCTION 62

Figure 4.17: Colour distribution of NGC 720 in blue with the generalized histogram ﬁt superimposed in black. The blue peak at (g-i) = 0.4, and the red peak at (g-i) = 0.6. The vertical red line marks the subdivision between the populations at (g-i) = 0.50. CHAPTER 4. DATA REDUCTION 63

by Sánchez-Blázquez et al. [2006]. The templates are synthetic, single-age, single- metallicity stellar populations that use a Salpeter IMF with a slope of 1.3. It is widely theorized that individual globular clusters form during one major burst, therefore, all the stars within the cluster are of similar metallicity and age. Under this assumption, using a single-age, single-metallicity SSPs is justified. The data of these empirically derived spectra are comprised of 985 stellar spectra taken with the 2.5 m INT telescope in La Palma. Their SEDs span a wavelength range of 3525-7500A˚ and have a moderately-high resolution of 2.3A˚ (FWHM). Available templates (276) span an age range of 0.10 - 17.78 Gyr and have metallicities [M/H] of -1.68, -1.28, -0.68, -0.38, 0.00, and +0.20. We selected 24 of the 276 templates, which span a range ages and metallicities; for each of the six forementioned metallicities we used a template of age 1.78 Gyr, 3.98 Gyr, 8.91 Gyr and 12.59 Gyr. The spectrum of each of the 24 templates is shown in Figure 4.18. These templates combine scaled-solar isochrone models with the MILES, where the models follow the chemical evolution pattern of the solar neighborhood. They have the advantage of relying on empirical parameters, on stellar spectra, and on extensive photometric libraries. This library is also more complete than stellar databases, the flux-calibration was performed very carefully and it offers a large wavelength range. The library offers low metallicity templates, which are of particular use for globular cluster studies [Vazdekis et al, 2010]. CHAPTER 4. DATA REDUCTION 64

Figure 4.18: The 24 templates used for the cross-correlation. The spectra from left to right go from ages 1.78, 3.98, 8.91 to 12.59 Gyr. From top to bottom the metallicity decreases beginning with +0.20, 0.00, -0.38, -0.68, -1.28 and -1.68 at the bottom. The wavelength in angstroms is at the bottom of the ﬁgure. CHAPTER 4. DATA REDUCTION 65

4.7.2 The fxcor task

The Fourier Cross-Correlation task, fxcor, uses the method developed by Tonry & Davis [1979], which uses fourier analysis to cross-correlate an input (object) list with a template (spectra) list. In this section, we brieﬂy describe the cross-correlation method. First, the data are logarithmically binned such that there is a linear velocity redshift. The task uses a uniform weighing for most values, but any deviations from zero are weighted quadratically. This means that strong lines exert a larger inﬂuence during minimization than weaker lines. The task minimizes equation 4.16 to determine the value of the Gaussian dispersion, σ2:

σ2 = µ2 − 2τ 2 (4.16) where µ is the dispersion of the largest peak centered at δ, and τ is the width of the peak. Equation 4.16 states that the average of the quadratic galaxy line widths with the template line widths is the width of the cross-correlation peak. The height of the peak determines α, the centre of the peak deﬁnes δ, and σ is the width of the peak in conjunction with the template width. The uncertainty in the redshift is determined by using a ”remainder function”. The c(x) function is assumed to be a perfect ﬁt such that any deviation from c(x) is a distortion having a parabolic peak (a(n)). If there are two potential peaks near a(n) with centers at n0 and n1, having second derivatives of d(n0) and d(n1), then the peak would be at n d + n d δ = 0 0 1 0 (4.17) d0 + d1 CHAPTER 4. DATA REDUCTION 66

2 where d0 is the true peak at −4h/w . Typically, a(n) would have a peak with width √ w and height 2σa. The mean distance between these peaks can now be estimated as: 1 1 N |n − n | ' · · (4.18) 1 0 2 2 2B where the highest wave number sets the value of B and c(n) is the value of the Fourier transform when it has an appreciable amplitude. Therefore, the mean error is

1 N 1 = (4.19) 4 2B 1 + r where h r = √ (4.20) 2σa

Equation 4.20 is called the Tonry & Davis R-value; it is a measure of the quality of the cross-correlation. Once a spectrum is correlated the heliocentric velocity is calculated:

wpc *shift vrelative = c ∗ (10 − 1) (4.21)

h v i v = 1 + template ∗ 10wpc *shift−1 ∗ c (4.22) observed c where wpc is scaling of the shift, shift is the wavelength shift determined from cross- correlation. The template velocities are all set to zero, therefore equation 4.21 and CHAPTER 4. DATA REDUCTION 67

equation 4.22 are equal. The heliocentric velocity is then described by

vhelio = vobserved + H(object) (4.23) where H(object) is the heliocentric correction for that observation.

4.7.3 Performing Cross-Correlation

When running the noao.rv.fxcor task use the following parameters:

• fxcor SpectrumList TemplateList continuum = both, filter = both, rebin = largest , osample = 4600-5400, rsample = 4600 - 5400, apodize = 0.2, function = gaussian, verbose = long, interactive = yes, observatory = gemini-south. The continuum fitting ensures that the continuum is removed in order improve the detection of spectral lines. We rebin the spectrum and the templates to the same dispersion. The input with the smaller dispersion is scaled up to the larger dispersion so as not to make the spectrum more precise than it really is. The osample and rsample parameters specify the range of wavelengths that we are interested in. This improves the quality of the cross-correlation since the task is not searching the wider spectral regions available with the templates. The peaks are specified to have gaussian-like shapes.

• continpars order = 8, niterate = 10. We found that with a higher order ﬁt we were able to better reproduce the object continuum and minimize the RMS. CHAPTER 4. DATA REDUCTION 68

• filtpars f type = ramp, cuton = 0, fullon = 100, cutoff = 900, fulloff = 1000

Each aperture was cross-correlated against each template. Features in the spectrum which did not seem to be due to emission or absorption lines were masked out and not included in the cross-correlation. For most of the apertures, filtering was turned off and continuum fitted was enabled at all times. We found that the filtering slightly increased the quality of the fit, especially for spectra which had low signal. Filtering was only turned on for spectra that had multiple potential correlation peaks in order to clear out the garbage signal. The final results consisted of 24 velocities recorded in the logfiles, which corresponded to the best cross-correlation (cleanest fit and highest TDR value) obtained using each template. During the cross-correlation, a comment was written for each spectrum. If a spectrum did not fit any of the templates well, it was flagged to be recorrelated using more stringent parameters. Usually this involved turning filtering on and off, playing with the continuum fitting, masking of bad spikes or, most likely, determining which of many potential peaks in very noisy data were the most promising and consistent. The fxcor task outputs the following information for each cross-correlation: the height of the peak, FWHM of peak, TDR value (goodness of fit), heliocentric velocity (vhelio) in km/s, observed velocity (vobs) in km/s, relative velocity (vrel) in km/s, uncertainty in velocity (verr) in km/s. From the description of the error analysis in Tonry & Davis [1979] and in the fxcor help manual, we decided that these errors were representative of the errors in the correlation and no further error analysis was performed on the velocities. However, CHAPTER 4. DATA REDUCTION 69

we were left with the task of deciding which of the 24 velocities was the best ﬁt to the spectrum. To decide this, we calculated the weighted mean (wvel) of the heliocentric velocity (vhelio) using the TDR as the weight, where a larger TDR value indicates a better ﬁt. The uncertainty in this velocity was determined by performing a weighted mean (werr) of the velocity uncertainty (verr), again using the TDR value as the weight. N X meanweighted = ai vhelio(i) (4.24) i=1 where [1 /TDR(i)] ai = (4.25) PN 2 i=1[1 /TDR(i) ] and N = 24, the number of template velocities obtained.

4.7.4 Velocity Results

In Figure 4.19 we show the weighted mean of the heliocentric velocity (wvel) plotted against the Tonry & Davis R-value for the whole sample set comprised of 241 objects. There were 6 objects that did not have suﬃcient signal to cross-correlate. The solid black horizontal line shows the systemic velocity of NGC 720 at 1745±7 km/s [Smith et al., 2000]. There is an obvious concentration of objects about this velocity, as well as about 0 km/s. This natural division is shown in Figure 4.20 using bins of 100 km/s. When we zoom into a velocity region bounded from -2000 to 10,000 km/s, as in Figure 4.21, this division becomes more distinct. There is a visible concentration of candidates about 1745 km/s and about 0 km/s, which would be foreground objects. For a GC candidate to be conﬁrmed as being associated with NGC 720, it must CHAPTER 4. DATA REDUCTION 70

have a heliocentric velocity between 500 - 3000 km/s. These limits were more or less arbitrarily determined based on Figure 4.20. These limits bound the outermost wings of the distribution profile about 1745 km/s. Outside of these limits, the detected objects are likely spurious detections because they do not appear to have velocities consistent with being bound to NGC 720. The confirmed clusters, bound by the velocity limits, can be seen in Figure 4.22. When observing the error bars in Figure 4.22, it is obvious that the size of the error bar is inversely proportional to the TDR value. This is encouraging and expected, because the candidates that had very high TDR values also had very consistent cross-correlation velocities. Conversely, if we look back to Figure 4.19, the high velocity candidates have low TDR values. These objects are likely background galaxies for which there was very little signal to cross-correlate against. As expected with the nature of such distant objects, their spectra would be different from that of a GC, and therefore, would not be expected correlate well with any of the templates. It is also possible that some of these objects are potential globular clusters bound to NGC 720, however the templates that we correlated against were insufficient to produce a good fit. We use no other means of rejecting a candidate GC. A TDR value less than 5 is denoted as a poor fit, but we keep several GCs with TDR<5 due to the small sample size.

4.7.5 Problem Candidates

We now re-examine the flagged clusters for which there was difficultly establishing a clear, distinct cross-correlation peak. The majority of these flagged candidates had two potential peaks. The fxcor task when identifying the peak would autoidentify CHAPTER 4. DATA REDUCTION 71

Figure 4.19: The weighted mean of the heliocentric velocity plotted against the Tonry & Davis R-value for the whole sample set (241 objects). The blue and red populations are shown by blue and red colours respectively. The colour division occurs at (g-i) = 0.50 (see Section 4.6.1) CHAPTER 4. DATA REDUCTION 72

30 All = 235 Bin size = 100 km/s

-1000 0 1000 2000 3000 Weighted Mean of Heliocentric Velocity (km/s)

Figure 4.20: The distribution of the weighted mean of the heliocentric velocity for the whole sample set. The vertical black line marks the systemic velocity of NGC 720. CHAPTER 4. DATA REDUCTION 73

Figure 4.21: The weighted mean of the heliocentric velocity plotted against the Tonry & Davis R-value for the whole sample size (241 objects). The blue and red populations are shown by blue and red colours respectively. The colour division occurs at (g-i) = 0.50 (see Section 4.6.1). Error bars are included in this ﬁgure. A TDR value less than 5 (the black vertical line) denotes a poor cross-correlation. CHAPTER 4. DATA REDUCTION 74

Figure 4.22: The weighted mean of the heliocentric velocity plotted against the Tonry & Davis R-value for conﬁrmed globular clusters. The blue and red populations are shown by blue and red colours respectively. The colour division occurs at (g-i) = 0.50 (see Section 4.6.1). A TDR value less than 5 (the black vertical line) denotes a poor cross-correlation. CHAPTER 4. DATA REDUCTION 75

one peak for some templates, and another for other templates. The resulting velocity output was difficult to analyze because the velocity determined was often hundreds of km/s different. To analyze these candidates, the fxcor task was run twice: once identifying only one of the two potential peaks, and then a second time only measuring the second peak. The two logfiles were then separated and run through the weighted average code again. Most spectra either did not fit the templates or the S/N was too low; these were rejected, resulting in fewer than 24 templates being taken into account for the average. The weighted average program treated the averaging calculations slightly differently than the well determined candidates because it took into account which of the two potential peaks had the most consistent velocity measurement, had the largest TDR value, and had the most number of data points. This significantly improved the quality of the fit allowing a reasonable velocity correlation. Figure 4.23 shows the distribution of TDR values for the confirmed objects in green and for the rejected objects in magenta. A TDR value less than 5 indicates a poor fit and is marked by a vertical line. As expected, most rejected objects fall at or under 5, however there are 5 candidates which have TDR values of 5 or less. The largest TDR value for a confirmed GC is 32 (F2Ap26). Figure 4.24 shows the velocities of each sample of objects with the candidates in green and rejected objects in magenta. The division between them in quite obvious, even when using relatively large bins of 100 km/s. CHAPTER 4. DATA REDUCTION 76

Figure 4.23: The distribution of Tonry & Davis R-value counts. Conﬁrmed objects are shown in green (120 conﬁrmed GCs) and rejected objects (115 objects) in magenta. CHAPTER 4. DATA REDUCTION 77

Figure 4.24: The velocity distribution centered on the candidacy region. Conﬁrmed objects are shown in green and rejected objects in magenta. The number of objects within each sample is shown in the top right corner. CHAPTER 4. DATA REDUCTION 78

4.8 Repeat Observations

The five fields of NGC 720 have overlapping regions, which suggests there are likely some candidate objects that have been observed more than once. To determine how consistent our measurements are between fields, we locate these objects and compare their data. To confirm an object as having a repeat observation we require that their transformed coordinates are essentially identical. We find the matching positions by sorting the astrometry list of all objects, including rejected objects, according to RA. We then identify objects which have very small differences in RA and Dec. Table 4.2 shows the RA and Dec of the astrometry matches. For each match, we checked the images to make sure it was, in fact, the same object. From here on we will refer to the matches in the following format: F#Ap#-F#Ap#. Match #1, F3Ap14-F1Ap49 (Field 3 Apt 14 and Field 1 Apt 49), is an uncertain match, however, the other matches are confirmed. The MDF mag is the magnitude of the object that was given in the MDF file. This is not the magnitude determined via aperture photometry. We flag the velocities corresponding to the astrometry matches in Table 4.2. To ensure the comparison of the very best cross-correlation velocities we compose a list of spectra containing of all the matches from Table 4.2 and re-ran fxcor on the potential matches. When we ran the cross-correlation we did not record the match pairs, therefore there could be no bias introduced. We used filtering for these objects. The results of the new cross-correlation velocities are shown in Table 4.3. The velocity is the weighted average of heliocentric velocities in km/s, the uncertainty is the werr parameter discussed in Section 4.7.4, which is the weighted average of CHAPTER 4. DATA REDUCTION 79

Table 4.2: Astrometry matches between ﬁelds

Match Field Aperture RA DEC MDF mag Label # [deg] [deg] [mag] 1 3 14 28.231398 -13.778970 13.535 uncertain 1 49 28.231202 -13.779290 13.498 2 3 3 28.240940 -13.750226 12.823 match 1 33 28.240705 -13.750485 12.835 3 3 2 28.256232 -13.745832 11.858 match 1 32 28.256018 -13.746042 11.932 4 3 1 28.258404 -13.741177 11.840 match 1 30 28.258185 -13.741409 11.815 5 3 7 28.261562 -13.763835 12.542 match 1 41 28.261412 -13.764027 12.530 6 3 18 28.267136 -13.772703 12.405 match 1 52 28.267039 -13.772911 12.428 7 1 51 28.267934 -13.761730 13.605 match 3 16 28.268077 -13.761569 13.629 8 3 5 28.273902 -13.743989 12.853 match 1 39 28.273748 -13.744145 12.835 9 3 6 28.274168 -13.748713 12.561 match 1 40 28.274029 -13.748860 12.572 CHAPTER 4. DATA REDUCTION 80

the fxcor uncertainty. The standard deviation is that of the weighted uncertainty. We also include the number of points calculated in the averages. The label column states whether the pair is a match. The mean of the diﬀerence in velocity matches is 13 km/s with a standard deviation of 12 km/s. This is consistent with there being no systematic problems with the velocites. Match 1 was not included in the average.

Table 4.3: Velocity matches between ﬁelds

Match Field Apt Velocity Uncertainty Stdev TDR No. pts Label # [km/s] [km/s] [km/s] 1 3 14 11518 36 4 5.97 20 uncertain 1 49 23038 45 7 6.42 11 2 3 3 -130 29 9 19.59 24 match 1 33 -137 23 1 12.02 24 3 3 2 1544 17 6 28.59 24 match 1 32 1516 18 6 27.31 24 4 3 1 1169 32 2 13.49 24 match 1 30 1148 19 4 22.96 24 5 3 7 1908 16 4 27.37 24 match 1 41 1887 16 3 27.24 24 6 3 18 1782 20 5 27.85 24 match 1 52 1774 21 7 23.32 24 7 1 51 1620 44 9 8.63 24 match 3 16 1699 41 9 9.80 24 8 3 5 1875 24 3 17.86 24 match 1 39 1864 27 2 17.19 24 9 1 40 -88 21 3 22.72 24 match 3 6 -56 25 28 5.50 24

Overall, all the matches except for the pair #1 in the ﬁrst row (F3Ap14-F1Ap49) are strong matches. I would argue that Match #1 is a match, but it is a background CHAPTER 4. DATA REDUCTION 81

object that does not cross-correlate well with the selected templates. Their spectra look similar with similar line strengths and both have 100-150 counts. The velocity matches are all well within each other’s uncertainties. We can now confidently compare velocities and positions between fields. Of these matches we have 6 candidate clusters, 2 likely foreground objects and one high redshift object. We therefore must remove the 12 double detections from the list of candidates and replace them with the averaged data for each of the 6 candidates. This decreases our list of candidate globular clusters to 120 potential objects. Of these confirmed clusters, 74 are members of the red GC population and 46 are members of the red GC population. This is the final number and list that will be used for the modeling. The Appendix contains three tables containing the full datasets for the photometry (Table A.1), velocity (Table A.2) and astrometry (Table A.3) results. Chapter 5

Modeling

5.1 Rotation in the GCS of NGC 720

We perform a non-linear least square ﬁtting method to ascertain if the GCS is rotating. First, we must calculate the azimuthal angles of all the clusters relative to the centre of NGC 720. We use the center of NGC 720 as stated in the NED database as RA: 01h53m00.5s, Dec: -13d44m19s (28.252167, -13.738667) at J2000.0 [Evans et al., 2010]. The azimuthal angle of the candidate clusters is measured east of north as positive, relative to the center of NGC 720. Using the spherical cosine rule and Figure 5.1 as a two-dimensional reference, we calculate the arc length between points O and G using equation 5.1.

¯ −1 ¯ ¯ ¯ ¯ rθ = OG = cos OP ∗ cos(GP ) + sin(OP ) ∗ sin(GP ) ∗ cos(∠OPG) (5.1)

82 CHAPTER 5. MODELING 83

Figure 5.1: Diagram for azimuthal angle calculations, where the origin, O, is the center of NGC 720 at (28.252167, -13.738667). Point G represents a GC with some position (a,b), and point P is the azimuth at (28.252167, 0).

¯ Using the Law of Sines we calculate the azimuthal angle (∠POG), or φr using OG.

∠ ¯ −1 sin( OPG) ∗ sin(GO) 180 φr =∠ POG = sin (5.2) sin(rθ) π

The distribution of azimuthal angles is shown in Figure 5.2; the data are folded to emphasize the periodic nature. The GCS appears to exhibit periodicity, with a large peak near 150◦ and a smaller peak around 300◦. The colour information from Section 4.6.1 is used to plot the azimuthal angle in Figure 5.3. The periodicity is more diﬃcult to observe in this Figure. The red GCs appear to cluster more about the systemic velocity than the blue GCs. There appears to be little periodicity for the blue clusters. CHAPTER 5. MODELING 84

Figure 5.2: The distribution of azimuthal angle for conﬁrmed GCs in degrees using 20◦ bins. The vertical dotted line marks 360◦ where we fold the data to demonstrate periodicity. CHAPTER 5. MODELING 85

Figure 5.3: Velocity of conﬁrmed GCs vs. azimuthal angle. The colour subdivision is made at (g-i) = 0.50. The blue squares represent blue GCs and the red triangles represent red GCs. The horizontal dashed line marks the systemic velocity of NGC 720 at 1745 km/s. The vertical dotted line marks 360◦ where the data are folded. CHAPTER 5. MODELING 86

5.1.1 Non-Linear Least Squares Fitting

We perform a non-linear least squares ﬁt to the data to determine the position angle and rotational velocity for each GC population. Equation 5.3 is minimized using the Numerical Recipes subroutines mrqmin, and mrqcalc, which apply the Levenberg- Marquardt Method of non-linear least squares ﬁtting [Press et al., 1992].

V (θ) = Vrot sin(θ − θ0) + V0 (5.3)

I wrote my own wrapper program for them called wrap.f90. The code minimizes for rotational velocity, Vrot, and position angle, θ0, while V0= 1745 km/s is held constant [Evans et al., 2010]. This method has been applied in numerous GCS studies, such as in Zepf et al. [2000], Côté et al. [2001] and Bridges et al. [2006]. Côté et al. [2001] discuss that this approach is valid for a spherical GCS because the projected angular velocity is constant on circles in the plane of the sky. If the angular velocity is constant on those circles, then the linear velocity has sinusoidal variations with respect to the projected azimuth. Norris et al. [2008] performed the same fit, additionally allowing V0 to vary. This approach can hide some rotation if the mean velocity of the GCs is not equal to V0. Input consists of a file containing the GC azimuthal angle (θ(n)), the velocity (Velocity(n)) and velocity uncertainty (werr(n)), along with initial guesses for the parameters being varied. The wrapper calls the Numerical Recipes subroutine mrqfind, which then calls on the gaussj subroutine to solve the covariance matrix, α, using Gauss-Jordan elimination. The solution , β, is sent back to mrqfind where it CHAPTER 5. MODELING 87

updates the guesses of Vrot and θ0. The new guesses are sent to subroutine mrqcalc which solves equation 5.3 for the new guesses, calculates a weighting function and

2 2 2 2 2 computes χ . If χ < χold then λ is decreased by 0.1. If χ > χold then λ is increased by 10. This process is iterated, using the weights applied to β, to update the covariance matrix α, until convergence below some tolerance level is reached. The λ parameter is a variable used to increase or decrease the variation between guesses, depending on the value of χ2, to allow for rapid convergence. We had some trouble getting this code to converge to the absolute minimum of the system. The initial guesses are critical in obtaining the absolute minimum of the solution instead of obtaining a local minima. To ensure that we were finding the absolute minimum, we created two versions of the code. Both versions use the same subroutines, but differ in their wrapper programs. The first version, wrap.f90, is the one described above, where the user makes initial guesses for Vrot and θ0. In the second version, LoopyWrap.f90, there is no user input apart from the input file.

This code loops through for all iterations of Vrot = 1-300 km/s in steps of 2 km/s

◦ ◦ ◦ 2 and θ0=1 -360 in steps of 1 , then returns χ for each iteration. The results for each iteration are output. The minimum χ2 of all these iterations is recorded in column 2-4 of Table 5.1. This process is obviously time consuming, however, it does ensure that the absolute minimum is located. To determine the uncertainty on the rotational velocity and position angle we conduct two tests called ErrorMethod1 and ErrorMethod2; these are reviewed below. CHAPTER 5. MODELING 88

ErrorMethod1

This method of measuring the uncertainty evaluates the ability for the code to re-

2 turn the original Vrot and θ0 for χmin when input data includes random variations that have been introduced to the velocity. The velocity is varied using a random factor of the velocity uncertainty, such that the uncertainty range of the velocity is never exceeded. The random number, between 0 and 1, is generated by Fortran 90’s intrinsic random seed function. This new velocity is associated with the same uncertainty and azimuthal angle as in the original dataset. While we considered varying the uncertainty in a Gaussian-like way, we decided against it. This method provides a maximum uncertainty range by introducing random noise into the data. In this way, we generated 500 datasets comprised of randomly varied velocities.

The LoopyNlsq.f90 code was run on these data for loops spanning Vrot=1-200 km/s

◦ ◦ 2 and θ0=1 -360 . The weighted mean of Vrot and of θ0 is calculated using χ as the weight. The uncertainty in Vrot and θ0 is calculated using the standard deviation about the weighted mean. The results of these uncertainty trials are summarized in Section [4] of Table 5.1. Table 5.1 summarizes the final results of the non-linear least squares fit. As a reminder, the whole dataset is comprised of 120 candidates, of which 46 are in the blue dataset and 74 are in the red. The Vrot and θ0 are the best fitting values

2 returned for χmin, the absolute minimum obtained using wrap.f90. Table 5.1 has four sections. The ﬁrst section, [1], contains results for minimizing both Vrot and

θ0 using the original data using wrap.f90. Unlike in the rest of the table, these uncertainties are the standard deviation of the weighted mean in the original data. CHAPTER 5. MODELING 89

The second section, [2], contains the results for minimizing Vrot while holding θ0 constant. The subscript H denotes the parameter held constant. The ”holding” value was determined by the results of section [1]. The third section, [3], contains the results for minimizing θ0 while holding Vrot constant. The fourth section, [4] contains the results for minimizing for both Vrot and θ0 using the 500 random datasets as the input, instead of the original data. All uncertainties were obtained using ErrorMethod1 except for Section 1. Except for the rotational velocity of the full GCS sample, all the corresponding values in each section agree with each other. The

GCS, therefore, has a Vrot = 50 ± 7 km/s. The blue population has a Vrot = 79 ± 14 km/s. The red population has a Vrot = 79 ± 7 km/s. The signiﬁcance of these rotational velocities and position angles will be discussed in Section 6.

Table 5.1: Summary for Results Obtained from Wrap.f90 and LoopyWrap.f90 code

Section Sample Vrot θ0 [km/s] [deg] [1] All 50 ± 7 170 ± 69 Blue 79 ± 7 89 ± 18 Red 97 ± 14 147 ± 18 [2] All 44 ± 23 170H Blue 80 ± 25 89H Red 89 ± 28 147H [3] All 50H 137 ± 57 Blue 79H 93 ± 17 Red 97H 136 ± 19 [4] All 34 ± 6 107 ± 64 Blue 73 ± 9 80 ± 24 Red 69 ± 17 130 ± 40 CHAPTER 5. MODELING 90

ErrorMethod2

How significant are these results? Down to what rotational velocity can we accurately say we can measure the rotational velocity and position angle? ErrorMethod2 addresses these questions by generating a noisy sinusoidal dataset with some specified position angle and specified rotational velocity. The scatter is introduced by applying a random fraction of the velocity uncertainties (from the real data) to the generated rotational velocity. This method of introducing scatter is the same method as that used for ErrorMethod1. The program generates a noisy sinusoidal dataset for each rotational velocity between 1-200 km/s, in steps of 1 km/s, to produce a total of 200 sample datasets. The azimuthal angles that are used are N evenly spaced points, where N = 120 for the GCS, N = 46 for the blue population and N = 74 for the red population. It should be noted that these values of the azimuthal angles are idealized since they vary linear steps. This method allows us to determine when the code can no longer yield significant results. The 200 generated datasets were run through the LoopyWrap.f90 code to determine at what rotational velocity the results are no longer significant. We do not test the azimuthal angles because they have such large uncertainties already. Ultimately, we are more interested in the rotational velocity. We calculate the %-difference of the output in relation to the actual Vrot. The results of the full sample are shown in Figure 5.4. If we allow a maximum of a 33% difference then, for a sample size of 120 objects, the non-linear least squares code is accurate down to a rotational velocity of Vrot ≈ 35km/s. We also ran the code for different values of θ0 and found that they did not have an effect on the significance of the code when it was held constant. CHAPTER 5. MODELING 91

The results for the red population are shown in Figure 5.5. For a sample size of 74 objects, the code is also accurate down to a rotational velocity of Vrot ≈ 35 km/s. The results for the blue population are shown in Figure 5.6. The non-linear least squares code beings to break down for a sample size of 46 objects. The curve begins to depart from ∼ 0% difference earlier than the red and full samples. The scatter is also significantly larger than that in the red and full sample results. At lower rotational velocities the ability of the code to determine the correct velocity dimishes. The rotational velocity of the whole GCS is on the edge of this limit, where Vrot = 50 ± 7 km/s, giving it a ∼ 10% maximum confidence level and 15% confidence at minimum. This 15% error in the code ability is just within the 7 km/s uncertainty yielded by ErrorMethod1. The red sample has a confidence level between 2 − 10%, which is within the uncertainty range for Vrot = 97 ± 14 km/s. The blue sample has a confidence level between ∼ 2 − 20% making the significance of the blue data variable.

5.1.2 Summary of Rotation Results

The whole GCS has a Vrot = 50 ± 7 km/s at a confidence level of 90%. The red population has a Vrot = 97 ± 14 km/s at a confidence level of 94%. The blue population has a Vrot = 79±7 km/s with a confidence level of 85%. The GCS rotational velocity is smaller than that of both the red and blue populations, indicating that the blue and red populations are out of phase. This is supported by individual results for

◦ ◦ each population. The position angle for the whole GCS is θ0 = 170 ± 69 , for the

◦ ◦ ◦ ◦ red population θ0 = 147 ± 18 and for the blue population θ0 = 89 ± 18 . Figure CHAPTER 5. MODELING 92

Figure 5.4: The %-Diﬀerence for the whole data sample. The horizontal line marks the 0% level. The vertical line is at a Vrot = 15 km/s which is where the results asymptotically begin to increase. CHAPTER 5. MODELING 93

Figure 5.5: The %-Diﬀerence for the red data sample. The horizontal line marks the 0% level. The vertical line is at a Vrot = 15 km/s which is where the results asymptotically begin to increase. CHAPTER 5. MODELING 94

Figure 5.6: The %-Diﬀerence for the blue data sample. The horizontal line marks the 0% level. The vertical line is at a Vrot = 10 km/s which is where the results asymptotically begin to increase. CHAPTER 5. MODELING 95

5.7 shows these best ﬁts for the rotational velocities of each population.

5.2 Velocity Dispersion

To estimate the velocity dispersion of NGC 720 we use a code Karl Gebhardt provided called dlowess.f [Gebhardt et al., 1994, 1995]. The dlowess code makes a lowess fit, (robust locally weighted scatterplot smoothing), to the velocity data. The lowess method was developed by Cleveland [1994]. Input consists of the galactocentric radius, velocity and velocity uncertainty, the smoothing factor, F, the minimum and maximum radius, and the systemtic velocity of the data. The method uses a nearest neighbor algorithm to fit the data to multiple polynomials. The smoothing factor is the fraction of points used in the polynomial, nearest neighbor fit; values between 0.2-0.5 are recommended. These polynomials are weighted according to a biweight function using a least squares method. The biweight function is defined by:

  u(1 − u2) for|u| ≤ 1 f(u) = (5.4)  0 for|u| > 1

where u is deﬁned by x(i) − Med u = (5.5) c ∗ Mad where Med is the median, Mad is the median absolute deviation from the median and c is a tuning constant which is set to 9.0 in the code. The weighted polynomials are then used to perform a locally weighted regression, which builds up the ﬁt piece by piece. These dispersion measurements do not take into account the rotation of CHAPTER 5. MODELING 96

Figure 5.7: Velocity vs. azimuthal angle for the final fits of the rotational velocity and position angle. The blue and red points represent the GCs. The red curve shows the best fit for Vrot = 97 ± 14 km/s for the red GCs, the blue curve shows the best fit for Vrot = 79±7 km/s for the blue GCs and the green curve shows the best fit for the whole GCS with Vrot = 50 ± 7 km/s. The horizontal dashed line marks the systemic velocity of NGC 720. CHAPTER 5. MODELING 97

the GC populations, however, the rotational velocities we calculate are small and should not signiﬁcantly aﬀect the dispersion calculations. We use a distance of 25.7 Mpc to scale the radius between the GC and the centre of NGC 720 [Tonry et al., 2001, Jensen et al., 2003]. The dlowess.f code calculates velocities for two options: the velocity (V) and the velocity dispersion (σ), where

σ = |Vsys − V (i)|. We find that a smoothing factor of F = 0.3 works best for the red and full populations; the sharp features are well smoothed, but some structure is still visible. We used F=0.5 for the blue population because it has fewer data points. Figure 5.8 shows the output for the velocity fit. The mean velocity for the whole GCS is 1738 ± 80 km/s. The blue GCs have a mean velocity of 1707 ± 77 km/s and the red GCs have a mean velocity of 1755 ± 74 km/s. These are in agreement with the systemic velocity of NGC 720 of 1745 ± 8 km/s [Smith et al., 2000]. Figure 5.9 shows the lowess fit for the velocity dispersion. The average velocity dispersion is 168 ± 22 km/s for the full GCS. The average velocity dispersion for the red GCs is 156 ± 30 km/s and is 181 ± 33 km/s for the blue GCs. These velocity dispersions are all consistent with being flat with increasing radius. The blue population appears to increase slightly for R > 20 kpc. The velocities of the confirmed clusters are included for reference in all plots as the blue or red points. Overall, the velocity dispersion of the blue GC population and the red GC population are approximately equal. CHAPTER 5. MODELING 98

Figure 5.8: Velocity vs. galactocentric radius for lowess fits with f = 0.3 for the red and full populations and f= 0.5 for the blue population. The bottom window is the fit to the whole sample, the middle window is for the blue GCs and the top window is for the red GCs. The solid black curve is the lowess fit. The horizontal line is the systemic velocity. CHAPTER 5. MODELING 99

Figure 5.9: The velocity dispersion (|∆σ|) vs galactocentric radius for a f=0.3 for the red and full population and f = 0.5 for the blue population. The bottom window is the lowess ﬁt for all the data. The middle window is for the blue GCs and top window is for the red GCs. CHAPTER 5. MODELING 100

5.3 Tracer Mass Estimator

The mass of the system can be estimated using a generalized version of the projected mass estimator, which follows the formula:

Cproj X M = v2 R (5.6) GN losi i i=1 where N is the number of objects in the system, G is the gravitational constant, R is the projected galactocentric radius and vlos is the line of sight velocity minus the systemic velocity of the galaxy. The Cproj factor is based on assumptions about the orbit: for a radial orbit C = 64/π and for an isotropic orbit C = 32/π [Bahcall & Tremaine, 1981, Perrett et al., 2002]. The underlying dark matter distribution is not necessarily the underlying mass distribution that a tracer population, such as GCs, PNe or halo stars, follow [Evans et al., 2003]. The number density of a tracer population has a power-law proﬁle:

aγ ρ(r) = ρ (5.7) 0 r where γ is the slope of the surface brightness proﬁle. To ﬁnd γ we use GC surface density data from Kissler-Patig et al. [1996] which extend to a radius of 408”. The Kissler-Patig et al. [1996] data contain only 12 values, of which 5 points measure the background level and one is an outlier. We supplement these data with stellar photometry from Goudfrooij et al. [1994] which extend to R ≈ 180” and includes 48 values. The data of Goudfrooij et al. [1994] is based on, and improves upon, CHAPTER 5. MODELING 101

the data of Peletier et al. [1990]. Combining the stellar surface brightness and GC distribution data is a less than ideal method, but they both have approximately the same slope and together they do constrain the slope much better than just fitting the 5 points from Kissler-Patig et al. [1996]. The position angle of the red GCs traces that of the galaxy light, indicating that the stellar light and GC distribution are aligned. If the difference between the GC distribution and the stellar surface brightness is significant, then the mass and M/L calculations will be affected. The stellar brightness profile is typically steeper than the GC number density profile, leading to an overestimate of the M/L ratio [Norris et al., 2008]. The Goudfrooij et al. [1994] data was measured in G-, R- and I-bands, but because Kissler-Patig et al. [1996] made their measurements in V-band, we use that. A scaling factor needs to be used to line up the data. We used Figure 5.10 to measure the slope of γ as 1.82. However, this is the projected slope, not the three-dimensional slope that we require. To deproject the slope we add 1.0 to obtain γ = 2.82. We calculate the tracer mass estimator using

C X M = v2 R (5.8) GN losi i i where 3−γ 4(α + γ) 4 − α − γ 1 − (rin/rout) C = 4−α−γ (5.9) π 3 − γ 1 − (rin/rout) where α is deﬁnes the gravitational potential and is set by astrophysical consider- ations. For example, α = 0 for an isothermal potential, α = 1 for a Keplerian CHAPTER 5. MODELING 102

Figure 5.10: Surface brightness proﬁle of NGC 720 in V-band using stellar data from Goudfrooij et al. [1994] in green and GCs data from Kissler-Patig et al. [1996] in blue. The red line is the ﬁt to the data to obtain a measure of γ. CHAPTER 5. MODELING 103

potential and if α = γ − 2, then the tracers follow the underlying dark matter potential [Evans et al., 2003]. The constant C is dimensionless, but the other parameters in equation 5.8 are not. To allow the units to work out you must multiply by a factor of 1.42063 × 109, which takes into account the units of the gravitational constant, the distance conversion, and the mass conversion. The galactocentric radius, Ri is calculated using the azimuthal angle code (Section 5.1). The code requires the input of the velocity, velocity uncertainty and galactocentric radius. We set rin = 1.0 kpc, and rout = 39.7 kpc, which are, respectively, the smallest and largest radii in the dataset. In addition to calculating the isotropic tracer mass estimator, we also calculate the anisotropic mass estimator, and projected mass estimator. The anisotropic mass estimator is calculated using equation 5.8, but with a diﬀerent constant C:

3−γ 16(α + γ − 2β) 4 − α − γ 1 − (rin/rout) C = 4−α−γ (5.10) π(4 − 3β) 3 − γ 1 − (rin/rout)

2 2 2 where the orbital anisotropy, β, is deﬁned by β = 1−σt /σr , where σt is the tangential

2 velocity dispersion and σr is the radial velocity dispersion. For radial orbits, β = 1.0 and for circular orbits, β = −∞ This anisotropic mass estimator is valid only for constant anisotropies. The projected mass estimator is calculated for isotropic orbits according to

32 X M = v2 R (5.11) proj πGN losi i i CHAPTER 5. MODELING 104

We calculate these masses for a range of α and β and use Table 5.2 to summa- rize the results. The uncertainty in the mass was calculated using the method of ErrorMethod1 in section 5.1.1. The velocity was randomly varied within the velocity uncertainty for 1000 realizations. We then ran the tracer mass code, taking the mass to be at the peak in the histogram. The uncertainty is the standard deviation about that peak. Columns 4 and 7 in Table 5.2 list the results of the uncertainty calculations. These results are also shown in Figure 5.11.

12 For a distance of 25.7 Mpc, the projected mass estimates Mproj = 2.91 × 10 M . The projected mass, however, should not be used for tracer populations because it systematically underestimates the enclosed mass [Evans et al., 2003]. This amounts to ∼ 10% error in the mass determination from the calculations alone and does not take into account any uncertainty in the α, β or distance parameters [Evans et al., 2003]. The measured masses using the isotropic tracer mass and the anisotropic tracer mass were summarized in Table 5.2. With the ∆Miso and ∆Maniso uncertainties in mind, these masses do encompass the mass range produced by diﬀerent values of β for constant α. The β = ±1.0 masses usually do not ﬁt within this range though. These averaged masses are in Table 5.3. CHAPTER 5. MODELING 105

Table 5.2: Summary of isotropic and anisotropic mass estimates for varying α and β parameters. We set γ = 2.82

Distance α Miso ∆Miso β Maniso ∆Maniso 12 12 12 12 [Mpc][×10 M ][×10 M ] [×10 M ][×10 M ] +0.7 +0.7 25.7 0.0 3.30 3.5−0.4 -1.0 3.22 3.4−0.4 25.7 0.0 3.30 0.0 3.30 25.7 0.0 3.30 0.2 3.33 +0.7 +0.7 25.7 0.0 3.30 3.5−0.4 0.4 3.38 3.6−0.5 25.7 0.0 3.30 0.6 3.45 +0.7 +0.7 25.7 0.0 3.30 3.5−0.4 0.8 3.57 3.8−0.5 25.7 0.0 3.30 1.0 3.85 +0.7 +0.6 25.7 0.2 2.98 3.1−0.4 -1.0 2.83 3.0−0.4 25.7 0.2 2.98 0.0 2.98 25.7 0.2 2.98 0.2 3.04 +0.7 +0.6 25.7 0.2 2.98 3.1−0.4 0.4 3.13 3.3−0.4 25.7 0.2 2.98 0.6 3.26 +0.7 +0.7 25.7 0.2 2.98 3.1−0.4 0.8 3.50 3.7−0.5 25.7 0.2 2.98 1.0 4.02 +0.6 +0.6 25.7 0.5 2.41 2.5−0.3 -1.0 2.20 2.3−0.3 25.7 0.5 2.41 0.0 2.41 25.7 0.5 2.41 0.2 2.49 +0.6 +0.5 25.7 0.5 2.41 2.5−0.3 0.4 2.61 2.8−0.4 25.7 0.5 2.41 0.6 2.80 +0.6 +0.7 25.7 0.5 2.41 2.5−0.3 0.8 3.12 3.3−0.4 25.7 0.5 2.41 1.0 3.83 +0.6 +0.6 25.7 γ − 2 1.75 1.8−0.1 -1.0 1.55 1.6−0.1 25.7 γ − 2 1.75 0.0 1.75 25.7 γ − 2 1.75 0.2 1.83 +0.6 +0.5 25.7 γ − 2 1.75 1.8−0.1 0.4 1.95 2.1−0.4 25.7 γ − 2 1.75 0.6 2.13 +0.6 +0.5 25.7 γ − 2 1.75 1.8−0.1 0.8 2.45 2.6−0.4 25.7 γ − 2 1.75 1.0 3.15 +0.5 +0.5 25.7 1.0 1.34 1.5−0.3 -1.0 1.21 1.3−0.3 25.7 1.0 1.34 0.2 1.46 +0.5 +0.5 25.7 1.0 1.34 1.5−0.3 0.5 1.64 1.7−0.1 +0.5 +0.6 25.7 1.0 1.34 1.5−0.3 1.0 2.65 2.8−0.4 CHAPTER 5. MODELING 106

Figure 5.11: The distribution of masses determined by the tracer mass code for 1000 diﬀerent datasets. The blue distribution represents the isotropic masses, the green distribution represents the anisotropic masses and the red distribution represents the projected masses. The top row of plots are for α = 0.0, the middle row for α = γ − 2 and the last row for α = 0.2. The ﬁrst column is for β = 0.4 and the second column is for β = 0.8. CHAPTER 5. MODELING 107

Table 5.3: Summary of averaged masses for varying β and constant α

12 α Mass [×10 M ] +0.7 0.0 3.5−0.4 +0.6 0.2 3.3−0.4 +0.5 0.5 2.8−0.4 +0.6 γ − 2 1.8−0.1 +0.5 1.0 1.5−0.3 Chapter 6

Discussion

We measured the velocities of 120 conﬁrmed GCs of which 46 are blue GCs and 74 are red GCs.

6.1 GC Rotation

The GCS of NGC 720 has an overall rotational velocity of 50±7 km/s with a position

◦ ◦ angle of θ0 = 170 ± 69 . The red population has a rotational velocity of 97 ± 14

◦ ◦ km/s with a position angle of θ0 = 147 ± 18 . The blue population has a rotational

◦ ◦ velocity of 79 ± 7 km/s with a position angle of θ0 = 89 ± 18 . Prior to this study, the red and blue GC populations had not been examined. We compare our results for the GCS to those of Kissler-Patig et al. [1996]. Kissler-Patig

◦ ◦ et al. [1996] measured a position angle for GCS at θ0 = 147 ± 10 and measured

◦ ◦ the position angle of the galactic light at θ0 = 142 ± 3 . Our GCS position angle is consistent with that of Kissler-Patig et al. [1996], however, it should be noted that

108 CHAPTER 6. DISCUSSION 109

our uncertainty bounds are large. The position angle of the red population allows for a better comparison, because the red population dominates the relative numbers of GCs. The position angle of the red population agrees with Kissler-Patig et al. [1996] for the GCS and for the galaxy light. Unlike the red population, the blue population is not aligned with the position angle of the galaxy. Blue GCs are more abundant at larger galactocentric radii than the red clusters. Some GCSs do not have significant rotation. Norris et al. [2008], for example, measured Vrot = 51.2 ± 36.7 km/s for 74 GCs in NGC 3923, where the 42 red GCs had Vrot = 37.6 ± 45.6 km/s, and the 32 blue GCs had Vrot = 70.5 ± 64.5 km/s; none of these velocities are statistically significant. The GCS of M 87 is an example of a GCS which does have significant rotational velocity (see Table 6.1). The rotational velocity of the GCS of NGC 720, in relation to other ellipticals with studied GCSs, is consistent with having a small rotation. For a summary of kinematic properties for a selection of galaxies with studied GCSs, see Table 6.1. The galaxies NGC 4636 and NGC 5128 both have similar magnitudes and effective radii compared to NGC 720, allowing for an accurate comparison to be made between them. The rotational velocity is small for the blue GC populations of NGC 4636, NGC 720 and NGC 5128. The red populations in all three of these galaxies have the largest rotational velocity in comparison to the GCS and blue population. The blue and red populations in NGC 4636 orbit in opposite directions to each other [Lee et al., 2010]. Of these three galaxies, NGC 720 has the most rapid rotational velocity of the GCS. This is interesting because NGC 5128 has a large population of blue stars, and like NGC 720, it is thought to be the product of a merger due to its dust lane. CHAPTER 6. DISCUSSION 110

NGC 4636 is a giant elliptical that is just beginning to fall into the Virgo cluster. It has radio jets, arm-like structures and has a cavity in X-ray emission near the central region [Lee et al., 2010]. The history and environment of both of these galaxies are very different from NGC 720. NGC 4636 is the only galaxy listed which has more observed red clusters than blue clusters. Whether the blue GCs were poorly sampled in our study or if there is a lack of blue GCs in NGC 720, we cannot say. Significant rotation should be observed in systems which underwent a recent (z<3) merger [Vitvitska et al., 2002]. If the angular momentum was transported at radii more distant than the extent of the optical data, then this can explain the low rotational velocity of GCS [Vitvitska et al., 2002]. Projection effects may play a role in not observing significant rotation. CHAPTER 6. DISCUSSION 111 et al. eff R Distance [2003], [4] = Bridges V -21.67 14.7 6.36 -22.65 16.05 et al. 4 4 30 65 43 22 -21.9 25.7 6.2 29 -22.13 16.8 7.33 33 13 13 9 12 18 9 ± ± +11 − +34 − +12 − +18 − +33 − +27 − ± ± ± ± ± ± 360 σ M [2010]. 300-200 ∼ 205 265 225 251 342 205 288 256 ∼ et al. 18 156 15 -22.3817 156 16.1 7.16 33 69 168 1528 149 -21.90 4.2 6.02 24 34 23 19 48 40 14 45 52 18 181 ◦ +53 − +16 − +18 − +73 − +37 − +12 − 0 144 ± ± ± ± ± ± ± ± [2010], [3] = Côté ± ± ± θ +146 − 65 105 313 0 178 218 237 174 100 225 76 59 et al. 77 170 89 53 130 51 46 38 14 147 10 185 15 196 35 154 1015 185 177 99 108 35 74 30 24 25 18 +62 − +58 − +50 − +120 − +51 − ± ± ± +48 − +76 − +32 − +34 − +52 − +68 − rot ± ± ± ± ± ± V [km/s] [deg] [km/s] [mag] [Mpc] [kpc] [2008], [5] = Schuberth 700 -21.71 20.0 7.34 ∼ et al. [2001], [2] = Woodley Red 46Red 97 130Red 68 119 160 Red 292 43 Red 105Red 12 38Red 171 61 Blue 74Blue 79 108Blue 27 161 172 Blue 291 26 Blue 158Blue 93 83Blue 130 110 et al. [2006], [6] = Hwang [1] = Côté M 87 [1] All 268 33 Galaxy Ref GC N NGC 720 All 120 50 NGC 4636 [6] All 238NGC 37 5128 [2] AllNGC 4472 564 [3] 33 AllNGC 4649 [4,6] 263 AllNGC 53 1399 121 [5] 141 All Table 6.1: Summary of kinematics data from other the GCSs of elliptical galaxies. References are as follows: CHAPTER 6. DISCUSSION 112

6.2 Velocity and Velocity Dispersion of the GCS

The red GCs have a mean velocity of 1755 ± 114 km/s, the blue GCs have a mean velocity of 1707 ± 77 km/s, and the mean velocity for the GCS is 1738 ± 80 km/s (see Figure 5.8). These mean velocities are all consistent with the systemic velocity of 1745±7 km/s. The uncertainty was determined by the standard deviation. There is a dip in the blue population to V ∼1500 km/s at 25 kpc. No corresponding dip is observed in the red population, but this dip has a slight effect on the fit for the full GCS. Overall these velocity profiles are consistent with being flat. In Figure 6.1 we show the direction of velocity with the position of the clusters.

The black dots mark GCs with Vrot < 1745 km/s and crosses mark GCs with Vrot > 1745 km/s. The red and blue circles denote their GC colour populations. Position angle is deﬁned as north of east is positive (counterclockwise rotation). There are 30 blue GCs with v < 1745 km/s (dot/blue), 34 red GCs with v < 1745 km/s (dot/red), 17 blue GCs with v > 1745 km/s (cross/blue) and 39 red GCs with v > 1745 km/s (cross/red). The breakdown of average velocities by population and position angle is in Table 6.2, which can help us determine if there is any mean motion of the GCs. A

◦ ◦ cross/red clump of clusters is observed near the galactic centre where θ0 ∼ 0 − 90 . This region of the galaxy, according to Table 6.2, has the highest density of GCs. These clusters have a mean motion > 1745 km/s. We could therefore expect that the opposite position angle at 180◦-270◦ have a mean motion < 1745 km/s if the clusters are orbiting in this direction. This motion is observed. The position angles

◦ ◦ ◦ ◦ between 90 < θ0 < 180 and 270 < θ0 < 360 have an overall lack of clusters and have mean motions < 1745 km/s. The blue GCs have a larger dispersion than the CHAPTER 6. DISCUSSION 113

red population which is consistent with their larger spatial distribution. We can see no evidence for counter rotation in these data.

Table 6.2: Average velocities corresponding to azimuthal angles and Figure 6.1

Region Population No. GCs Velocity 0◦-90◦ All 48 1760 Red 30 1767 Blue 18 1855 90◦-180◦ All 24 1708 Red 10 1734 Blue 13 1672 180◦-270◦ All 36 1721 Red 22 1729 Blue 14 1709 270◦-360◦ All 12 1663 Red 7 1788 Blue 5 1487

An increasing velocity profile at large radii can indicate that orbits are anisotropic [Woodley et al., 2010]. A peak in the velocity profile at the systemic velocity indicates that GCs follow very radial orbits. The profile will have a flattened shape if the GCs follow circular orbits [Woodley et al., 2010]. A Gaussian distribution will be observed if the GCs follow isotropic orbits [Woodley et al., 2010]. Simulations by Dekel et al. [2005] suggest that the orbits of tracer populations should be isotropic at small galactocentric radii and change to anisotropic orbits at large radii. Our velocity profiles are slightly too noisy to comment on this. This requires further modeling. The velocity dispersion profile is shown in Figure 5.9. This dispersion is not corrected for rotation in the GC populations. The red population has a mean velocity CHAPTER 6. DISCUSSION 114

Figure 6.1: The positions of the conﬁrmed GCs with colour and velocity information. The large blue circles mark positions of blue GCs and the large red circles mark the positions of red GCs. The black points and crosses denote velocity information. A cross marks a GC with a velocity > 1746 km/s and a point marks a GC with a velocity < 1745 km/s. The dotted horizontal and vertical lines mark the centre of the galaxy. CHAPTER 6. DISCUSSION 115

dispersion of 156 ± 30 km/s, the blue population has a mean dispersion of 181 ± 33 km/s. The full GCS has a mean dispersion of 168 ± 22 km/s. These profiles are all consistent with being flat as radius increases, suggesting that a dark matter halo is present. There are three blue GCs with high velocity at radii of ∼ 40 kpc which appear to be causing the velocity dispersion of the blue population to increase towards larger radii. These objects may be unbounded to NGC 720. More data are needed at larger radii to constrain the dispersion profile. The dispersion of the red GCs is less than that of the blue GCs. This is particularly evident at larger radii where the increase in velocity dispersion is observed for the red population. The dispersion of the GCS is raised slightly due to this. Both populations do not seem to be more or less dynamically hot than the other. If the source of the red GC population is a merger event ∼ 4 Gyr ago, then sufficient time has elapsed for the dynamics to cool down. X-ray observations, discussed in sections 3.2 and 3.3 support this. GCS studies of elliptical galaxies (M87, NGC 4472, NGC 1399, NGC 5128, NGC 3379) consistently indicate that the velocity dispersion is either constant or increasing with galactocentric radius [Bridges et al., 2006]. We observe this to also be the case for NGC 720. Table 6.1 summarizes the velocity dispersion for other GCSs. As in section 6.1, we continue to compare NGC 720 to NGC 4636 and NGC 5128. The velocity dispersion between NGC 5128 and NGC 720 are the most similar. As in NGC 4636, the velocity dispersion of the blue clusters is larger than the red. The velocity dispersion of NGC 720 is lower than other ellipticals with similar masses and luminosities. This may be due to the isolated environment of NGC 720. The fundamental plane correlates the luminosity, velocity dispersion and size of elliptical CHAPTER 6. DISCUSSION 116

galaxies. An elliptical, such as NGC 720, which has a smaller velocity dispersion can also be expected to have a smaller eﬀective radius and luminosity. However, the isolated environment of NGC 720 may skew this empirical relation.

6.3 Mass

As stated in Chapter 3.4, Humphrey et al. [2010] found that within a radius of ∼ 30 kpc the shape of the total gravitating mass proﬁle is an isothermal sphere. Our GCs trace out to a maximum radius of 40 kpc. With this in mind, comparing to an isothermal, α = 0.0, model is valid. Our velocity dispersion results do not reveal any information about the anisotropy, therefore, we will make a guess on the anisotropy based on the comparison of other published masses and our results.

Using VT0 = 10.17 mag from Table 3.1 we calculate LV :

mV, − MV = 5 log (d/10pc) (6.1) MV = −21.9

−0.4(M−MV, ) L/L = 10 (6.2) 10 LV = 4.96 × 10 L where MV, = +4.84 [Binney & Merriﬁeld , 1998].

For an isothermal potential, the isotropic tracer mass estimator calculates Miso =

+0.7 12 +21 3.5−0.4 × 10 M , yielding a M/LV = 70−12. If, instead, we calculate M/L using

+0.6 12 +10 M = 1.8−0.1 × 10 M for α = γ − 2, then we obtain M/L = 30−12. These two masses span the maximum and minimum ranges of our computed masses, therefore CHAPTER 6. DISCUSSION 117

12 M(R < 40) = (1.7 − 4.1) × 10 M and M/LV ≈ 30 − 70. A more state-of-the-art, in depth analysis of the mass and M/L ratio will be conducted in future work using the orbit-based models of Gebhardt et al. [2003] and Thomas et al. [2005].

12 Our mass models that are closest to the stellar mass of 3.68 × 10 M , measured by Rembold et al. [2005], are for an α = γ − 2 potential. The mass corresponding to

12 α = γ −2 and the range of anisotropies, is Mγ−2 = (1.7−2.4)×10 M . We estimate from Figure 6 in Humphrey et al. [2010] that within an enclosing radius of 40 kpc,

12 they measure a mass of 0.8×10 M . The value of β that would best agree with this mass would be for β = 0. Based on this, we suggest that the GC orbits are isotropic and trace the dark matter potential out to a radius of 40 kpc. For reference, Table 6.3 contains a summary of past mass estimates for NGC 720. The mass, enclosed radius, and M/L from Humphrey et al. [2006] were also estimated oﬀ of graphs. The eﬀective radius of NGC 720 is 1Re = 6.2 kpc = 52”.

+0.6 12 We estimate a mass of Mγ−2 = 1.8−0.1 × 10 M for a potential that traces the dark matter and β = 0.0. For an isothermal potential, we estimate Miso =

+0.7 12 3.5−0.4 × 10 M . Overall, it appears that the M/L ratio of NGC 720 is increasing with radius. At the centre of the galaxy, van der Marel [1991] measure the stellar M/LB = 3.68, then

Buote et al. [2002] measure M/LB ≈ 7 − 22 increasing between radii R = 6-18 kpc.

Humphrey et al. [2010] estimate M/LV ≈ 13 and our research indicates that at R =

40 kpc, M/LV = 30 − 70. Our M/L estimate may be an overestimate because of the surface brightness proﬁle we used to calculate γ, however, under the assumption that γ is roughly correct, the M/L will still be suﬃciently large to support this increasing CHAPTER 6. DISCUSSION 118

Table 6.3: Summary of past estimates of the mass and M/L of NGC 720.

Reference Band Luminosity Distance Enclosing Mass M/L Radius 10 12 [×10 L ] [Mpc] [kpc] [×10 M ] [M /L ] van der Marel B 41.0 1Re ≈ 7 [1991] −2 −1 Buote et al. B 3.3h70 25h70 1Re 6.0 h70 [2002] B 2Re 12.3h70 B 3Re 18.7h70 Humphrey et al. K 40 ≈ 1 ≈ 20 [2006] (Fig. 5,6) Humphrey et al. V 25.7 40 ≈ 0.8 ≈ 13 [2010] (Fig. 6) +0.6 +10 Present Studyα=γ−2 V 4.96 25.7 40 1.8−0.1 30−12 +0.7 +21 Present Studyα=0 V 4.96 25.7 40 3.5−0.4 70−12

component of dark matter at larger radii.

For comparison, the M/LV = 6 − 7 of NGC 3923 is roughly constant out to

R ∼ 1Re, then it increases to M/LV = 20 at R = 5Re, indicating it contains significant amounts of dark matter [Norris et al., 2008]. Côté et al. [2001] estimate the red GCs in M 87 are on radial orbits with β ' 0.4 and the blue GCs have tangential orbits with β ' −0.4 for a mean velocity ellipsoid that is isotropic. Lee et al. [2010] were able to determine that the GCs follow tangential orbits in NGC 4636. There are several problems with tracer mass estimations. The first is that the radii are projected, therefore they are subject to projection uncertainties. The γ parameter must be chosen very precisely in order to well determine the mass. We must assume spherical symmetry and for any anisotropic models, the anisotropy must CHAPTER 6. DISCUSSION 119

be assumed to be constant with radius. When testing the tracer mass code, Evans et al. [2003] found that the projected mass, calculated in this way, is underestimated. The use of tracer mass estimators also requires that the system is in steady state equilibrium and that the tracers actually do follow the gravitational potential. NGC 720, in its isolated environment, would support this criterion. Using PNe as tracers, for example, may not be possible with new research indicating that the velocity proﬁles of PNe decrease with increasing radius. GCs, however, are much better tracers.

6.4 Implications

The rotation signature of the GCS of NGC 720 is weak, but significant. The blue and red populations both have rotational velocities that are similar but have different position angles. The position angles have less statistical significance. The velocity dispersion is constant with increasing radius for both populations. The dispersion of the blue population shows some increase towards larger radii, but more data are needed to confirm this. These velocity dispersions are consistent with requiring a dark matter halo. Both Miso and Mγ−2 are on the high end of other estimates for NGC 720, however, there is little consistency in the mass estimates. With our masses on the high end of the scale, it comes as no surprise that our M/LV estimates are also high. In terms of formation models, we can make a few observations which agree or disagree with GCS properties that may be expected for a particular scenario. The CHAPTER 6. DISCUSSION 120

monolithic collapse model would not produce high rotational velocities for either population, however those of NGC 720 are not that large. If this model would be able to produce such velocities, other parameters, such as bimodality, would still be unexplained. The merger model is more promising when compared to the kinematics of NGC 720. The simulations by Bekki et al. [2005] indicate the blue population would have larger rotation and be more spatially extended than the red population; this is partly observed. The blue population is more extended, but the red population has the larger rotational velocity. The simulations also predict that the red population traces the isophotal major axis. While our position angles are not

◦ very certain, we do obtain θ0 = 147 ± 18 , which is in agreement with the position angle of the galaxy light at 142 ± 3◦ [Kissler-Patig et al., 1996]. The multiphase dissipational collapse model would induce little rotation on the blue population, while the rotation on the red population would be dependent on the dissipation of collapse [Forbes et al., 1997, Lee et al., 2010]. These predictions could explain our results. The blue population can be explained, but the red population may have diﬃculty. How much rotation would the collapse be able to impart on the red population? Finally, the dissipationless accretion model predicts that the blue population would have larger spatial extension, be on radial orbits and have no rotation. Our observations agree with the ﬁrst condition, we have no information on the second condition and the third condition fails. According to Hwang et al. [2008] blue GCs in a typical massive collapse, would exhibit large velocity dispersion instead of having a large rotational velocity. The dispersion of the red population should be less than the blue population [Hwang et CHAPTER 6. DISCUSSION 121

al., 2008]. This is observed with σ = 181 ± 33 km/s for the blue population, and σ = 156 ± 30 km/s for the red population, however within the uncertainties, these dispersion are essentially equal. It should be noted that Bekki et al. [2005] used no rotation, only dispersion in the initial conditions of the simulations. Research indicates that a 4:1 merger occurred roughly 4 Gyr ago. This is supported by the twist in the X-ray isophotes and a younger population of stars in the central region of the galaxy. The kinematics of the GCS are dynamically cool with no irregularites and apart from the X-ray isophote twist, the isophotes are relaxed. Likely, suﬃcient time has past for the system to relax. NGC 720 shows evidence for residing in a massive dark matter halo. Chapter 7

Summary and Conclusions

7.1 Summary

Using Gemini/GMOS spectroscopy we have obtained spectra for 241 objects in the isolated elliptical NGC 720. We have extracted spectra and performed cross- correlation to determine velocities for 120 confirmed GCs. Aperture photometry was used to measure the colour bimodality, which was then used to separate the metal- rich from the metal-poor GCs at (g-i) = 0.50. We find 46 metal-poor, blue, GCs and 76 metal-rich, red GCs in the sample of 120 confirmed GCs.

The GCS population has a rotational velocity of Vrot = 50 ± 7 km/s at a signiﬁ-

◦ ◦ cance level of 10%, with a position angle of θ0 = 170 ± 69 . The mean velocity for the GCS is 1738 ± 80 km/s. The GCS has an average velocity dispersion of 168 ± 22 km/s.

The red GC population has a rotation velocity of Vrot = 97 ± 14 km/s at a

122 CHAPTER 7. SUMMARY AND CONCLUSIONS 123

◦ ◦ signiﬁcance level of 6%, with a position angle of θ0 = 147 ± 18 . The mean velocity is 1755 ± 74 km/s. The average velocity dispersion for the red GCs is 156 ± 30 km/s.

The blue GC population has a rotational velocity of Vrot = 79 ± 7 km/s at a

◦ ◦ signiﬁcance level of 15%, with a position angle of θ0 = 89 ± 18 . The mean velocity is 1707 ± 77 km/s with an average velocity dispersion of 181 ± 33 km/s.

+0.7 12 We estimate the mass within 40 kpc is 3.5−0.4×10 M for an isothermal potential

+0.6 12 and is 1.8−0.1 × 10 M for a potential that traces the dark matter. These masses bound a corresponding M/LV ≈ (30 − 70)M /L . Although we can make no strong conclusions about the anisotropy, we can state that masses estimated using isotropic orbits best agree with previously published values.

7.2 Future Work

We will be using axisymmetric, orbit-based models to perform more rigorous modeling of the gravitational potential of NGC 720 [Gebhardt et al., 2003, Thomas et al., 2005]. Stellar spectroscopy have been obtained using the Visible Integral-Field Replicable Unit Spectrograph (VIRUS-P) at the McDonald Observatory in late 2010. These stellar kinematics will be used in conjunction with the GC kinematics presented in this research and with existing X-ray data to obtain a more complete picture of the gravitational potential of NGC 720. A study is also underway that will present more accurate photometry. This study will use the same Gemini data as presented here. More spectroscopic data needs to be obtained for GCs in order to expand the sample sizes of the blue and red populations to enable more detailed CHAPTER 7. SUMMARY AND CONCLUSIONS 124

modeling. More GC data at large galactocentric radii would be of particular use- fulness. If we hope to decrease the uncertainties of our measurements to something closer to those of NGC 5128 in Table 6.1 for example, then we will need spectroscopic observations for another ∼400 clusters. Additional studies of group ellipticals and ﬁeld ellipticals should be studied to bridge the gap in galaxy environments.

7.3 Conclusion

The flat velocity dispersion profiles suggest that NGC 720 has a massive dark matter halo. The tracer mass estimates indicate that the gravitational potential is isothermal with isotropic dispersion. The models presented in this work are not sophisticated enough to make any independent conclusions on the shape of the dark matter potential. The spatial distributions of the blue and red populations about the centre of the galaxy are consistent with NGC 720 undergoing a merger at some time, however sufficient time has elapsed for the dynamics of the metal-rich population to cool. We suggest that the orbits are isotropic. Few studies of GCS have been performed in isolated environments, therefore it is difficult to say if any properties are charac- teristic of a GCS, however it is interesting to note that NGC 4636, which we were comparing with, is very similar, but it is infalling to the Virgo Cluser. Bibliography

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Tables of Data

Table A.1: Photometry Results: In the Comments column, R and C indicate whether an object is rejected (R) from candidacy or is conﬁrmed (C) as a GC. IP and GP indicate that there were problems obtaining photometry for either the i-band or g-band image.

Field Ap g-band g-band i-band i-band (g-i) (g-i) Comments error error colour error 1 1 18.185 0.008 17.493 0.008 0.692 0.008 R 1 2 20.452 0.040 20.101 0.064 0.351 0.052 C 1 3 17.059 0.036 16.399 0.036 0.660 0.036 C 1 4 17.011 0.043 16.338 0.043 0.673 0.043 R 1 5 23.205 0.497 20.527 0.099 2.678 0.298 C 1 6 19.145 0.097 18.321 0.084 0.824 0.091 R 1 7 19.377 0.026 18.889 0.036 0.488 0.031 R 1 8 18.551 0.013 18.257 0.020 0.294 0.017 C 1 9 21.367 0.098 21.513 0.259 -0.146 0.179 C 1 10 17.536 0.006 17.624 0.014 -0.088 0.010 C 1 11 18.119 0.009 18.060 0.016 0.059 0.013 C 1 12 18.346 0.008 17.871 0.013 0.475 0.011 C 1 13 22.259 0.203 22.464 0.553 -0.205 0.378 C 1 14 18.347 0.008 17.666 0.009 0.681 0.009 C 1 15 18.773 0.012 16.997 0.005 1.776 0.009 R 1 16 19.432 0.030 18.885 0.034 0.547 0.032 C 1 17 18.168 0.018 16.495 0.008 1.673 0.013 R 1 18 20.940 0.062 20.107 0.068 0.833 0.065 C 1 19 19.311 0.013 18.380 0.013 0.931 0.013 C 1 20 20.930 0.075 20.631 0.131 0.299 0.103 C 1 21 22.735 0.481 20.466 0.128 2.269 0.305 C Continued on Next Page. . . 134 APPENDIX A. TABLES OF DATA 135

Table A.1 – Photometry Results Continued

Field Ap gmag gerr imag ierr gimag gierr Comments 1 22 20.821 0.092 19.822 0.061 0.999 0.077 C 1 23 18.374 0.013 17.797 0.015 0.577 0.014 C 1 24 18.039 0.065 17.285 0.064 0.754 0.065 C 1 25 18.310 0.009 17.364 0.008 0.946 0.009 R 1 26 18.935 0.020 18.307 0.023 0.628 0.022 C 1 27 18.769 0.012 17.769 0.011 1.000 0.012 C 1 28 18.791 0.011 18.432 0.019 0.359 0.015 R 1 29 19.169 0.041 18.685 0.052 0.484 0.047 C 1 30 19.541 0.076 18.991 0.084 0.550 0.080 R 1 31 18.604 0.012 18.033 0.015 0.571 0.014 C 1 32 12.506 0.016 11.760 0.017 0.746 0.017 R 1 33 18.723 0.008 17.399 0.006 1.324 0.007 R 1 34 19.646 0.018 19.272 0.028 0.374 0.023 R 1 35 19.484 0.021 19.113 0.031 0.371 0.026 R 1 36 19.226 0.024 18.656 0.031 0.570 0.028 R 1 37 21.876 0.143 23.070 0.948 -1.194 0.546 C 1 38 18.541 0.025 17.875 0.027 0.666 0.026 R 1 39 17.392 0.052 16.746 0.056 0.646 0.054 R 1 40 19.503 0.022 19.421 0.043 0.082 0.033 R 1 41 19.138 0.048 18.523 0.053 0.615 0.051 R 1 42 21.426 0.096 23.150 1.000 -1.724 0.548 R,IP 1 43 19.639 0.019 18.552 0.015 1.087 0.017 R 1 44 19.212 0.013 18.497 0.016 0.715 0.015 C 1 45 18.287 0.018 18.274 0.031 0.013 0.025 R 1 46 19.255 0.021 18.878 0.031 0.377 0.026 C 1 47 18.833 0.054 17.957 0.048 0.876 0.051 C 1 48 22.060 0.158 25.489 8.124 -3.429 4.141 R 1 49 18.246 0.043 17.861 0.059 0.385 0.051 R 1 50 18.343 0.051 17.690 0.055 0.653 0.053 C 1 51 18.609 0.032 17.595 0.024 1.014 0.028 R 1 52 19.529 0.035 19.111 0.050 0.418 0.043 R 1 53 16.356 0.028 15.625 0.029 0.731 0.029 R 1 54 21.877 0.184 21.966 1.000 -0.089 0.592 R,IP 1 55 19.835 0.039 19.388 0.054 0.447 0.047 C 1 56 21.818 0.231 22.729 1.114 -0.911 0.673 R 2 1 18.715 0.011 18.382 0.020 0.333 0.016 R 2 2 19.975 0.032 18.263 0.018 1.712 0.025 R 2 3 17.368 0.003 17.417 0.008 -0.049 0.006 R 2 4 21.317 0.118 20.011 0.097 1.306 0.108 R 2 5 20.937 0.067 20.045 0.079 0.892 0.073 R 2 6 17.650 0.005 17.475 0.011 0.175 0.008 R 2 7 17.573 0.022 16.984 0.027 0.589 0.025 C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 136

Table A.1 – Photometry Results Continued

Field Ap gmag gerr imag ierr gimag gierr Comments 2 8 18.409 0.018 17.899 0.024 0.510 0.021 C 2 9 19.114 0.016 18.529 0.022 0.585 0.019 C 2 10 18.867 0.012 18.635 0.021 0.232 0.017 C 2 11 18.555 0.026 17.917 0.030 0.638 0.028 C 2 12 18.343 0.047 17.998 0.070 0.345 0.059 C 2 13 18.931 0.025 18.364 0.029 0.567 0.027 C 2 14 20.206 0.044 17.538 0.014 2.668 0.029 R 2 15 21.361 0.096 19.868 0.072 1.493 0.084 R 2 16 21.213 0.089 21.133 0.302 0.080 0.196 C 2 17 20.065 0.034 18.951 0.034 1.114 0.034 R 2 18 19.642 0.024 19.039 0.033 0.603 0.029 C 2 19 20.161 0.039 19.020 0.032 1.141 0.036 C 2 20 19.592 0.024 18.862 0.032 0.730 0.028 C 2 21 19.373 0.029 19.562 0.068 -0.189 0.049 C 2 22 21.139 0.087 21.466 0.250 -0.327 0.169 R 2 23 20.178 0.046 19.854 0.085 0.324 0.066 R 2 24 19.321 0.076 19.102 0.123 0.219 0.100 C 2 25 18.219 0.008 17.422 0.009 0.797 0.009 R 2 26 17.721 0.006 17.086 0.007 0.635 0.007 C 2 27 18.351 0.050 17.948 0.074 0.403 0.062 C 2 28 17.711 0.020 17.417 0.032 0.294 0.026 R 2 29 16.935 0.044 16.256 0.048 0.679 0.046 C 2 30 20.171 0.028 18.694 0.029 1.477 0.029 R 2 31 20.642 0.050 22.844 1.000 -2.202 0.525 R,IP 2 32 20.195 0.032 19.464 0.043 0.731 0.038 R 2 33 19.967 0.043 19.741 0.084 0.226 0.064 R 2 34 18.946 0.051 18.527 0.068 0.419 0.060 C 2 35 18.589 0.013 18.016 0.015 0.573 0.014 R 2 36 18.860 0.017 18.330 0.020 0.530 0.019 C 2 37 20.951 0.060 19.960 0.077 0.991 0.069 R 2 38 21.492 0.126 20.995 0.296 0.497 0.211 R 2 39 19.087 0.015 18.534 0.021 0.553 0.018 C 2 40 20.486 0.169 19.141 0.097 1.345 0.133 R 2 41 21.196 0.070 20.108 0.083 1.088 0.077 R 2 42 20.352 0.036 20.543 0.112 -0.191 0.074 R 2 43 19.387 0.021 18.859 0.027 0.528 0.024 C 2 44 19.291 0.018 18.717 0.022 0.574 0.020 R 2 45 18.821 0.012 18.418 0.017 0.403 0.015 R 2 46 19.168 0.013 18.691 0.020 0.477 0.017 C 2 47 19.125 0.018 18.500 0.019 0.625 0.019 C 2 48 20.422 0.037 20.758 0.145 -0.336 0.091 R 2 49 19.912 0.035 19.112 0.041 0.800 0.038 R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 137

Table A.1 – Photometry Results Continued

Field Ap gmag gerr imag ierr gimag gierr Comments 2 50 22.290 0.277 19.914 0.076 2.376 0.177 R 2 51 20.174 0.036 19.595 0.046 0.579 0.041 C 2 52 19.795 0.021 18.718 0.022 1.077 0.022 R 3 1 16.982 0.031 16.273 0.034 0.709 0.033 R 3 2 19.850 0.024 19.004 0.020 0.846 0.022 R 3 3 17.018 0.043 16.269 0.044 0.749 0.044 R 3 4 19.708 0.066 19.182 0.075 0.526 0.071 C 3 5 18.993 0.014 18.310 0.013 0.683 0.014 R 3 6 17.793 0.004 17.377 0.007 0.416 0.006 R 3 7 18.600 0.014 18.177 0.018 0.423 0.016 R 3 8 17.755 0.005 17.155 0.006 0.600 0.006 C 3 9 18.136 0.009 18.017 0.016 0.119 0.013 R 3 10 18.381 0.009 17.662 0.009 0.719 0.009 C 3 11 17.454 0.004 17.410 0.007 0.044 0.006 R 3 12 19.033 0.020 18.419 0.021 0.614 0.021 C 3 13 19.548 0.023 18.739 0.022 0.809 0.023 R 3 14 19.464 0.035 19.083 0.044 0.381 0.040 R 3 15 19.188 0.017 18.655 0.018 0.533 0.018 R 3 16 20.278 0.050 22.875 0.968 -2.597 0.509 R 3 17 21.071 0.069 21.701 0.259 -0.630 0.164 C 3 18 18.609 0.007 18.304 0.015 0.305 0.011 R 3 19 16.899 0.003 16.535 0.004 0.364 0.004 R 3 20 21.964 0.162 19.882 0.058 2.082 0.110 R 3 21 17.073 0.003 17.025 0.004 0.048 0.004 C 3 22 20.843 0.061 19.031 0.026 1.812 0.044 R 3 23 24.035 1.180 21.285 0.280 2.750 0.730 R 3 24 17.950 0.004 17.539 0.006 0.411 0.005 R 3 25 21.412 0.136 18.471 0.025 2.941 0.081 C 3 26 18.812 0.009 17.999 0.009 0.813 0.009 R 3 27 20.352 0.033 19.991 0.052 0.361 0.043 C 3 28 18.412 0.014 17.992 0.019 0.420 0.017 C 3 29 18.544 0.019 17.772 0.017 0.772 0.018 C 3 30 18.371 0.007 17.669 0.007 0.702 0.007 R 3 31 18.777 0.024 18.376 0.032 0.401 0.028 C 3 32 20.063 0.031 19.282 0.031 0.781 0.031 R 3 33 16.817 0.002 16.832 0.004 -0.015 0.003 C 3 34 19.071 0.018 18.349 0.021 0.722 0.020 R 3 35 22.319 0.254 20.292 0.080 2.027 0.167 R 3 36 19.590 0.019 18.853 0.019 0.737 0.019 C 3 37 18.265 0.017 18.233 0.034 0.032 0.026 R 3 38 17.857 0.006 16.719 0.004 1.138 0.005 R 3 39 20.268 0.037 18.767 0.019 1.501 0.028 R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 138

Table A.1 – Photometry Results Continued

Field Ap gmag gerr imag ierr gimag gierr Comments 3 40 19.182 0.015 18.826 0.027 0.356 0.021 C 3 41 18.154 0.083 17.332 0.079 0.822 0.081 R 3 42 22.571 0.361 18.475 0.017 4.096 0.189 C 4 1 19.162 0.058 18.859 0.091 0.303 0.075 C 4 2 18.527 0.017 17.916 0.022 0.611 0.020 C 4 3 17.235 0.003 16.285 0.002 0.950 0.003 R 4 4 18.892 0.053 18.051 0.051 0.841 0.052 C 4 5 19.351 0.028 18.974 0.036 0.377 0.032 C 4 6 19.248 0.037 18.464 0.035 0.784 0.036 C 4 7 19.220 0.019 18.594 0.020 0.626 0.020 C 4 8 18.825 0.013 17.889 0.010 0.936 0.012 R 4 9 18.368 0.009 17.624 0.010 0.744 0.010 C 4 10 18.415 0.012 17.790 0.013 0.625 0.013 C 4 11 22.850 1.000 22.699 0.926 0.151 0.963 R,GP 4 12 19.174 0.051 18.266 0.045 0.908 0.048 R 4 13 16.919 0.003 15.428 0.002 1.491 0.003 R 4 14 19.587 0.024 19.198 0.031 0.389 0.028 C 4 15 24.320 1.857 22.739 1.000 1.581 1.429 R,IP 4 16 23.318 0.651 21.128 0.177 2.190 0.414 R 4 17 22.410 1.000 22.654 0.726 -0.244 0.863 R,GP 4 18 22.142 1.000 23.509 1.332 -1.367 1.166 R,GP 4 19 22.494 0.286 26.049 2.000 -3.555 1.143 R,IP 4 20 17.636 0.064 17.057 0.080 0.579 0.072 C 4 21 17.919 0.046 17.216 0.052 0.703 0.049 C 4 22 23.008 0.407 23.274 1.000 -0.266 0.704 R,IP 4 23 18.456 0.019 17.987 0.024 0.469 0.022 C 4 24 18.464 0.049 18.048 0.068 0.416 0.059 C 4 25 15.819 0.034 15.036 0.036 0.783 0.035 C 4 26 15.340 0.037 14.479 0.035 0.861 0.036 C 4 27 16.223 0.037 15.457 0.038 0.766 0.038 R 4 28 18.406 0.054 17.598 0.053 0.808 0.054 C 4 29 14.510 0.026 13.350 0.025 1.160 0.026 C 4 30 19.267 0.052 18.478 0.048 0.789 0.050 C 4 31 20.235 0.044 19.566 0.050 0.669 0.047 R 4 32 20.172 0.034 19.921 0.056 0.251 0.045 C 4 33 18.086 0.014 17.667 0.020 0.419 0.017 C 4 34 18.657 0.038 17.818 0.036 0.839 0.037 C 4 35 18.215 0.039 17.354 0.036 0.861 0.038 C 4 36 18.998 0.012 18.641 0.017 0.357 0.015 C 4 37 19.081 0.050 18.382 0.052 0.699 0.051 C 4 38 19.105 0.046 18.395 0.048 0.710 0.047 C 4 39 18.958 0.048 18.370 0.056 0.588 0.052 C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 139

Table A.1 – Photometry Results Continued

Field Ap gmag gerr imag ierr gimag gierr Comments 4 40 21.388 0.097 23.115 1.000 -1.727 0.549 R,GP 4 41 16.941 0.002 16.639 0.002 0.302 0.002 R 4 42 19.026 0.024 18.383 0.024 0.643 0.024 C 4 43 18.371 0.008 17.472 0.007 0.899 0.008 R 4 44 19.226 0.022 18.742 0.028 0.484 0.025 C 4 45 18.257 0.008 17.483 0.007 0.774 0.008 R 4 46 19.546 0.029 19.194 0.042 0.352 0.036 C 4 47 18.324 0.008 17.403 0.007 0.921 0.008 C 4 48 18.886 0.012 18.488 0.017 0.398 0.015 C 4 49 25.685 4.451 24.481 1.000 1.204 2.726 R,GP 4 50 22.965 0.359 24.137 2.333 -1.172 1.346 R 5 1 24.420 1.000 22.470 0.726 1.950 0.863 R,GP 5 2 25.760 2.000 21.935 0.414 3.825 1.207 R,GP 5 3 19.420 0.016 19.039 0.026 0.381 0.021 C 5 4 24.216 1.326 22.533 0.658 1.683 0.992 R 5 5 19.598 0.021 19.108 0.031 0.490 0.026 C 5 6 19.118 0.013 18.100 0.013 1.018 0.013 R 5 7 23.705 1.000 23.136 0.964 0.569 0.982 R,GP 5 8 17.173 0.052 16.564 0.061 0.609 0.057 C 5 9 18.873 0.013 18.438 0.019 0.435 0.016 R 5 10 19.385 0.017 19.059 0.028 0.326 0.023 C 5 11 19.299 0.068 18.727 0.076 0.572 0.072 C 5 12 19.305 0.027 18.929 0.041 0.376 0.034 R 5 13 21.363 0.094 21.313 0.203 0.050 0.149 R 5 14 25.246 1.000 30.000 10.000 -4.754 5.500 R,GP,IP 5 15 17.455 0.003 17.401 0.005 0.054 0.004 R 5 16 19.223 0.033 18.615 0.038 0.608 0.036 R 5 17 19.229 0.082 18.632 0.095 0.597 0.089 C 5 18 26.264 2.000 23.278 2.000 2.986 2.000 C,GP,IP 5 19 22.408 0.259 25.150 1.000 -2.742 0.630 C,IP 5 20 20.058 0.036 19.898 0.068 0.160 0.052 C 5 21 19.646 0.023 19.117 0.030 0.529 0.027 C 5 22 19.682 0.021 19.193 0.030 0.489 0.026 C 5 23 19.050 0.015 18.269 0.015 0.781 0.015 R 5 24 23.397 0.589 23.504 1.000 -0.107 0.795 C,IP 5 25 19.208 0.012 18.482 0.013 0.726 0.013 R 5 26 18.372 0.006 17.704 0.007 0.668 0.007 R 5 27 17.696 0.047 16.934 0.048 0.762 0.048 R 5 28 18.051 0.054 17.423 0.064 0.628 0.059 C 5 29 19.312 0.017 18.937 0.026 0.375 0.022 R 5 30 18.794 0.013 18.101 0.015 0.693 0.014 R 5 31 19.333 0.028 19.006 0.042 0.327 0.035 R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 140

Table A.1 – Photometry Results Continued

Field Ap gmag gerr imag ierr gimag gierr Comments 5 32 17.512 0.021 16.938 0.025 0.574 0.023 C 5 33 18.956 0.013 18.542 0.019 0.414 0.016 C 5 34 20.122 0.026 19.561 0.036 0.561 0.031 C 5 35 19.252 0.062 18.813 0.087 0.439 0.075 R 5 36 16.883 0.040 16.281 0.048 0.602 0.044 R 5 37 22.725 0.277 17.876 0.025 4.849 0.151 C 5 38 19.897 0.025 19.524 0.037 0.373 0.031 R 5 39 15.808 0.040 15.092 0.043 0.716 0.042 C 5 40 19.256 0.068 18.742 0.082 0.514 0.075 R 5 41 17.428 0.037 16.680 0.037 0.748 0.037 R

Table A.2: Velocity Results: The Quality column comments on the quality of the cross-correlation fit where G = good quality, P = poor, Pf = initially poor but has been fixed. The Comments column states whether a spectra is a confirmed (C) GC or if it was rejected (R).

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments value (km/s) (km/s) (km/s) 1 1 20.33 210.01 34.41 14.37 24 G R 1 2 15.61 1523.61 37.58 7.02 24 G C 1 3 4.80 1322.92 69.52 6.98 23 G C 1 4 3.21 1583.51 96.20 10.30 35 G R 1 5 4.33 1632.77 82.21 9.08 11 Pf C 1 6 2.16 -1829.10 139.85 0.00 1 G R 1 7 2.95 8620.43 66.50 3.74 10 Pf R 1 8 19.82 1920.42 22.90 6.74 24 G C 1 9 11.47 2058.94 31.57 2.58 24 G C 1 10 6.29 1321.95 54.27 5.61 15 Pf C 1 11 16.16 2144.93 23.05 2.55 24 G C 1 12 20.00 1730.19 18.86 4.30 24 G C 1 13 19.69 110.11 32.49 12.81 24 G C 1 14 14.61 1857.74 24.88 5.78 24 G C 1 15 2.89 10481.43 51.60 2.42 13 Pf R 1 16 13.15 2363.22 29.40 2.05 24 G C 1 17 6.28 -12816.15 39.56 3.60 15 Pf R 1 18 8.99 2040.81 37.92 7.01 16 Pf C 1 19 31.65 1624.04 16.94 6.10 24 G C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 141

Table A.2 – Velocity Results Continued

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments 1 20 10.44 2101.17 42.52 10.50 24 G C 1 21 17.08 1841.41 23.27 4.94 24 G C 1 22 16.24 1911.21 16.94 3.41 24 G C 1 23 25.34 1850.61 14.46 4.31 24 G C 1 24 12.36 1843.34 25.03 4.41 24 G C 1 25 21.05 -134.04 38.63 10.29 24 G R 1 26 30.79 1140.72 19.29 7.58 24 G C 1 27 5.05 1767.03 59.18 3.91 24 G C 1 28 3.42 -13797.33 63.45 1.73 6 Pf R 1 29 21.20 1457.92 18.55 3.15 25 G C 1 30 22.96 1148.21 18.53 4.09 24 G R 1 31 25.76 1744.22 88.45 44.23 24 G C 1 32 27.31 1515.80 18.41 6.17 24 G R 1 33 12.02 -136.52 23.01 0.93 24 Gr R 1 34 7.72 -7525.21 60.63 7.25 24 G R 1 35 9.69 32.17 64.75 20.64 24 G R 1 36 4.33 6572.63 39.11 2.42 24 G R 1 37 15.35 1470.67 22.01 3.50 24 G C 1 38 4.73 -8204.74 64.72 5.36 19 Pf R 1 39 17.19 1864.31 26.57 2.30 24 G R 1 40 22.72 -88.31 21.25 3.25 24 G R 1 41 27.24 1886.76 15.95 3.19 24 G R 1 42 7.41 5362.15 159.88 26.29 13 Pf R 1 43 8.65 5329.60 107.21 21.49 16 Pf R 1 44 5.90 1606.82 45.36 3.39 24 G C 1 45 11.73 139.22 65.66 24.42 24 G R 1 46 7.44 1670.38 37.33 6.08 24 G C 1 47 12.50 1454.83 28.01 8.85 24 G C 1 48 4.91 -6310.69 63.54 12.16 24 G R 1 49 6.42 23037.92 44.65 7.40 11 Pr R 1 50 12.20 1755.20 23.87 3.61 26 G C 1 51 8.63 1620.20 44.49 8.96 24 G R 1 52 23.32 1773.86 21.20 7.07 24 G R 1 53 4.22 -14904.85 111.83 14.52 15 Pf R 1 54 3.99 -4715.98 89.88 16.00 24 G R 1 55 11.89 1704.77 31.73 1.67 24 G C 1 56 5.64 11445.97 70.75 12.77 24 G R 2 1 5.42 8924.90 64.28 2.62 15 Pf R 2 2 5.04 9883.69 29.73 2.43 14 Pf R 2 3 19.41 259.35 30.09 4.75 24 G R 2 4 6.48 -11036.30 44.62 10.24 24 G R 2 5 12.53 -7943.20 31.29 7.25 18 Pf R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 142

Table A.2 – Velocity Results Continued

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments 2 6 13.16 -139.65 34.23 4.29 24 G R 2 7 23.19 1845.26 23.14 6.55 24 G C 2 8 15.48 1534.59 26.81 7.42 24 G C 2 9 10.45 1743.12 28.24 4.38 24 G C 2 10 8.81 871.13 27.36 1.11 16 Pf C 2 11 15.37 1818.36 28.90 4.98 24 G C 2 12 12.94 1843.51 49.66 7.77 24 G C 2 13 10.66 2047.80 40.07 9.16 24 G C 2 14 10.25 101.84 94.76 34.64 24 G R 2 15 3.88 -2063.35 48.56 3.24 24 G R 2 16 5.58 2722.07 58.70 7.63 17 Pf C 2 17 7.40 10497.93 69.18 7.71 11 Pf R 2 18 6.12 1691.54 43.50 4.80 24 G C 2 19 5.38 1909.26 38.65 4.28 24 G C 2 20 6.42 2299.65 24.65 2.42 13 Pf C 2 21 5.37 1641.84 59.23 9.58 9 Pf C 2 22 5.62 -10088.78 36.49 4.89 24 G R 2 23 6.36 -11193.20 24.33 1.38 24 G R 2 24 5.25 2046.57 35.66 1.57 24 G C 2 25 24.47 177.10 13.11 2.38 24 G R 2 26 31.99 1567.04 9.76 2.23 24 G C 2 27 16.21 1618.71 16.29 1.91 24 G C 2 28 21.80 -164.28 17.37 2.97 24 G R 2 29 12.76 1447.59 20.74 1.95 24 G C 2 30 8.82 -103.24 31.37 1.44 24 G R 2 31 5.23 1960.80 70.71 8.82 11 Pf R 2 32 10.79 10654.58 80.58 8.15 10 Pf R 2 33 6.17 -16024.00 32.26 2.50 20 Pf R 2 34 5.28 2047.99 52.68 5.41 23 Pf C 2 35 14.43 84.20 21.08 3.24 24 G R 2 36 10.15 1532.58 30.34 2.40 24 G C 2 37 4.30 -14355.06 41.21 5.77 24 G R 2 38 5.31 -8061.30 40.58 10.36 24 G R 2 39 17.16 1539.81 38.81 13.20 24 G C 2 40 4.71 8219.84 29.70 0.87 12 Pf R 2 41 5.29 -7173.68 43.27 3.20 9 Pf R 2 42 5.52 -13330.54 49.02 4.60 24 G R 2 43 5.46 1881.04 61.99 8.18 17 Pf C 2 44 6.54 7968.79 25.82 1.45 8 Pf R 2 45 6.82 -1594.56 28.25 3.07 24 G R 2 46 8.31 1881.18 26.20 2.36 24 G C 2 47 8.56 1432.84 27.86 2.89 24 G C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 143

Table A.2 – Velocity Results Continued

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments 2 48 7.21 -110.38 35.99 6.39 24 G R 2 49 4.09 6649.89 39.63 1.79 18 Pf R 2 50 5.24 -21708.53 28.14 1.24 17 Pf R 2 51 5.82 2006.53 29.99 1.30 19 Pf C 2 52 7.17 220.17 37.63 3.93 20 Pf R 3 1 13.49 1169.08 32.16 2.29 24 G R 3 2 28.59 1543.74 17.46 6.21 24 G R 3 3 19.59 -130.24 29.34 9.12 24 Gr R 3 4 7.61 1553.27 33.46 3.46 24 G C 3 5 17.86 1874.85 24.22 2.89 24 G R 3 6 28.35 -55.51 25.18 5.50 24 G R 3 7 27.37 1907.72 15.51 3.89 24 G R 3 8 13.62 1819.68 31.44 4.10 24 G C 3 9 12.40 29155.16 115.69 24.89 12 Pf R 3 10 16.94 1688.05 30.31 10.66 24 Pf C 3 11 4.18 -8437.47 71.70 9.03 5 Pf R 3 12 9.98 1814.56 25.77 3.09 24 G C 3 13 4.97 8313.95 70.72 16.83 20 Pf R 3 14 5.97 11517.62 35.73 3.90 20 Pr R 3 15 5.48 -9589.88 63.58 8.25 15 Pf R 3 16 9.80 1698.89 41.34 9.04 24 G R 3 17 27.21 1713.86 21.90 8.10 24 G C 3 18 27.85 1782.10 19.60 4.68 24 G R 3 19 27.95 -25.06 26.03 7.56 24 G R 3 20 6.38 28012.31 74.37 8.21 11 Pf R 3 21 24.33 1713.83 16.29 2.29 24 G C 3 22 7.42 -12351.10 79.69 19.98 15 Pf R 3 23 4.72 -5282.92 47.49 5.30 11 Pf R 3 24 9.24 -4073.82 75.25 8.11 10 Pf R 3 25 16.88 1660.12 23.32 3.12 24 G C 3 26 23.71 229.15 20.90 6.41 24 G R 3 27 13.94 1496.01 22.89 2.21 24 G C 3 28 12.09 1670.37 39.07 11.50 24 G C 3 29 18.96 1854.12 32.86 11.46 24 G C 3 30 6.36 18142.46 68.45 7.55 14 Pf R 3 31 23.08 1857.80 21.05 6.41 24 G C 3 32 4.60 -9414.46 55.71 3.64 16 Pf R 3 33 12.65 1621.91 48.37 10.24 24 G C 3 34 26.38 -146.70 34.86 9.04 24 G R 3 35 10.59 10560.48 124.06 17.38 10 Pf R 3 36 15.56 1507.92 22.97 6.06 24 G C 3 37 4.79 -16140.16 44.89 2.22 17 Pf R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 144

Table A.2 – Velocity Results Continued

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments 3 38 3.60 7518.86 64.33 4.07 16 Pf R 3 39 9.04 8010.64 144.92 20.90 13 Pf R 3 40 5.00 2545.73 61.69 3.38 14 Pf C 3 41 6.00 -107.77 34.78 2.23 14 Pf R 3 42 14.05 1719.48 27.74 5.25 24 G C 4 1 5.46 1262.56 41.62 7.55 22 Pf C 4 2 9.47 1934.31 44.75 10.82 24 G C 4 3 20.81 -43.23 45.28 20.14 24 G R 4 4 12.65 1678.45 32.97 4.26 24 G C 4 5 6.21 1918.89 112.14 26.34 21 Pf C 4 6 8.54 1837.55 51.60 9.78 24 G C 4 7 8.53 1799.87 35.76 3.56 24 G C 4 8 5.45 -4408.47 62.01 5.08 17 Pf R 4 9 25.07 1811.05 33.02 14.33 24 G C 4 10 18.96 1945.73 27.11 7.47 24 G C 4 11 1.27 9949.33 212.62 22.25 9 Pf R 4 12 4.56 -4342.89 48.07 9.48 17 Pf R 4 13 8.10 -12833.35 224.97 34.04 12 Pf R 4 14 5.93 1668.91 62.93 7.29 14 Pf C 4 15 2.43 -13594.29 60.66 3.75 16 Pf R 4 16 2.62 11762.46 58.25 3.55 15 Pf R 4 17 2.49 2508.65 54.45 4.59 14 Pf R 4 18 3.23 6570.45 97.65 23.25 14 Pf R 4 19 3.34 -470.34 59.40 4.62 11 Pf R 4 20 5.19 1862.17 52.68 11.34 24 G C 4 21 7.61 1942.25 50.03 12.11 16 Pf C 4 22 6.38 -338.38 39.39 2.68 24 G R 4 23 13.33 1618.78 25.32 4.06 24 G C 4 24 15.23 1808.22 20.05 1.93 24 G C 4 25 8.42 1659.51 31.42 4.46 24 G C 4 26 10.55 2136.99 41.89 6.39 24 G C 4 27 3.59 -13024.44 107.99 5.81 14 Pf R 4 28 8.44 1718.21 37.97 6.50 24 G C 4 29 6.33 2080.10 38.84 10.38 24 G C 4 30 11.42 1686.45 38.21 8.14 24 G C 4 31 8.39 28982.97 168.75 14.62 11 Pf R 4 32 5.55 1563.40 64.87 9.44 15 Pf C 4 33 15.38 1858.70 30.21 4.44 24 G C 4 34 13.83 1756.59 31.27 9.20 24 G C 4 35 10.93 1944.74 28.84 3.24 24 G C 4 36 6.07 1355.34 66.19 10.96 24 G C 4 37 12.68 2028.49 25.71 4.91 24 G C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 145

Table A.2 – Velocity Results Continued

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments 4 38 13.99 4439.40 45.17 28.11 24 G C 4 39 12.13 2148.41 30.03 3.41 24 G C 4 40 3.33 20807.24 67.32 5.93 16 Pf R 4 41 20.73 -148.45 18.73 4.09 24 G R 4 42 17.39 1854.75 23.07 3.61 24 G C 4 43 6.67 -230.53 49.68 11.14 24 G R 4 44 18.05 1722.29 37.87 12.37 24 G C 4 45 22.27 205.09 25.51 7.87 24 G R 4 46 6.09 2037.85 56.11 8.35 23 Pf C 4 47 11.09 1923.69 41.27 9.45 24 G C 4 48 4.77 1533.48 54.35 6.87 19 Pf C 4 49 4.25 -14712.95 67.01 8.28 14 Pf R 4 50 3.94 -7445.08 56.74 2.58 13 Pf R 5 1 7.09 4559.99 39.89 3.65 14 Pf R 5 2 4.77 -1465.09 67.24 5.40 19 Pf R 5 3 3.39 1369.12 83.49 12.97 22 Pf C 5 4 3.84 18311.93 94.51 28.89 17 Pf R 5 5 7.42 1659.92 51.60 7.70 24 G C 5 6 3.66 5642.26 85.34 11.52 14 Pf R 5 7 5.29 -12864.88 55.49 8.39 21 Pf R 5 8 8.74 1403.73 76.81 5.60 24 G C 5 9 5.39 -5633.32 61.85 8.92 18 Pf R 5 10 5.74 1741.51 46.23 8.72 24 G C 5 11 13.88 1536.34 32.74 5.59 24 G C 5 12 1.88 8050.75 74.17 3.48 17 G R 5 13 3.75 6885.74 58.44 7.80 24 G R 5 14 4.06 1597.16 71.31 7.89 23 Pf R 5 15 3.06 8010.43 57.23 1.64 15 Pf R 5 16 4.77 15293.28 92.75 9.18 12 Pf R 5 17 16.50 1534.62 29.04 5.33 24 G C 5 18 16.17 1872.63 38.48 12.62 24 G C 5 19 13.11 1664.06 30.83 7.10 24 G C 5 20 14.27 1845.12 39.89 8.02 24 G C 5 21 10.47 1420.62 48.89 10.58 24 G C 5 22 9.03 1611.97 43.77 4.06 24 G C 5 23 7.52 1929.02 50.70 5.68 24 G C 5 24 10.47 -7656.17 23.93 1.87 14 Pf R 5 25 6.87 1977.54 42.43 5.62 21 G C 5 26 6.37 -8400.47 33.02 2.54 14 Pf R 5 27 4.69 1915.74 45.40 3.25 24 G R 5 28 25.18 1661.67 34.36 14.80 24 G C 5 29 4.35 -13689.39 38.81 1.44 14 Pf R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 146

Table A.2 – Velocity Results Continued

Field Apt TDR Velocity Uncertainty stdev err pts Quality Comments 5 30 4.53 -11739.80 48.92 4.29 10 Pf R 5 31 3.60 -18999.70 71.30 10.08 21 G R 5 32 9.60 1539.43 38.81 3.60 24 G C 5 33 6.96 1369.62 41.86 5.42 24 G C 5 34 13.55 1712.21 36.80 11.70 24 G C 5 35 5.06 -15272.87 60.90 6.99 21 G R 5 36 6.46 6664.52 55.07 4.43 24 G R 5 37 5.10 1421.25 57.13 4.58 14 Pf C 5 38 21.34 -85.83 40.81 9.13 24 G R 5 39 5.89 1741.69 44.00 6.03 24 G C 5 40 3.31 -8874.14 70.40 7.00 12 Pf R 5 41 3.77 -10363.56 104.48 42.20 24 G R

Table A.3: Astrometry Results: The i-band and g-band astrometry is included even though all the analysis was performed using the g-band data. The comments column speciﬁes whether an object was conﬁrmed (C) as a GC or rejected (R).

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 1 1 1 : 52 : 56.28 -13 : 42 : 29.22 1 : 52 : 56.28 -13 : 42 : 28.81 R 1 2 1 : 53 : 0.800 -13 : 40 : 59.38 1 : 53 : 0.800 -13 : 40 : 58.87 C 1 3 1 : 52 : 58.63 -13 : 41 : 12.72 1 : 52 : 58.64 -13 : 41 : 12.22 C 1 4 1 : 52 : 49.55 -13 : 43 : 16.62 1 : 52 : 49.54 -13 : 43 : 16.23 R 1 5 1 : 52 : 47.80 -13 : 43 : 23.13 1 : 52 : 47.79 -13 : 43 : 22.73 C 1 6 1 : 52 : 47.36 -13 : 44 : 7.860 1 : 52 : 47.34 -13 : 44 : 7.53 R 1 7 1 : 52 : 58.95 -13 : 41 : 41.91 1 : 52 : 58.96 -13 : 41 : 41.45 R 1 8 1 : 52 : 57.88 -13 : 42 : 2.130 1 : 52 : 57.88 -13 : 42 : 1.69 C 1 9 1 : 52 : 55.92 -13 : 42 : 10.68 1 : 52 : 55.92 -13 : 42 : 10.25 C 1 10 1 : 52 : 46.54 -13 : 44 : 7.780 1 : 52 : 46.53 -13 : 44 : 7.44 C 1 11 1 : 52 : 56.93 -13 : 42 : 28.50 1 : 52 : 56.93 -13 : 42 : 28.10 C 1 12 1 : 52 : 55.99 -13 : 42 : 47.90 1 : 52 : 55.99 -13 : 42 : 47.51 C 1 13 1 : 52 : 59.58 -13 : 42 : 19.09 1 : 52 : 59.58 -13 : 42 : 18.69 C 1 14 1 : 52 : 57.91 -13 : 42 : 46.36 1 : 52 : 57.91 -13 : 42 : 45.98 C 1 15 1 : 52 : 58.97 -13 : 42 : 41.36 1 : 52 : 58.98 -13 : 42 : 40.99 R 1 16 1 : 52 : 58.81 -13 : 42 : 49.92 1 : 52 : 58.81 -13 : 42 : 49.56 C 1 17 1 : 52 : 48.55 -13 : 44 : 45.94 1 : 52 : 48.54 -13 : 44 : 45.67 R 1 18 1 : 52 : 48.49 -13 : 44 : 56.41 1 : 52 : 48.48 -13 : 44 : 56.16 C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 147

Table A.3 – Astrometry Results Continued

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 1 19 1 : 52 : 58.26 -13 : 43 : 35.99 1 : 52 : 58.26 -13 : 43 : 35.69 C 1 20 1 : 52 : 59.14 -13 : 43 : 18.54 1 : 52 : 59.14 -13 : 43 : 18.22 C 1 21 1 : 52 : 56.03 -13 : 43 : 40.30 1 : 52 : 56.03 -13 : 43 : 39.99 C 1 22 1 : 52 : 57.05 -13 : 43 : 58.08 1 : 52 : 57.04 -13 : 43 : 57.80 C 1 23 1 : 52 : 56.06 -13 : 43 : 48.10 1 : 52 : 56.06 -13 : 43 : 47.81 C 1 24 1 : 52 : 57.92 -13 : 43 : 7.770 1 : 52 : 57.92 -13 : 43 : 7.43 C 1 25 1 : 53 : 2.200 -13 : 43 : 24.02 1 : 53 : 2.20 -13 : 43 : 23.74 R 1 26 1 : 52 : 59.80 -13 : 43 : 44.49 1 : 52 : 59.80 -13 : 43 : 44.21 R 1 27 1 : 52 : 58.92 -13 : 43 : 49.41 1 : 52 : 58.92 -13 : 43 : 49.14 C 1 28 1 : 52 : 52.94 -13 : 45 : 15.50 1 : 52 : 52.93 -13 : 45 : 15.31 R 1 29 1 : 53 : 6.060 -13 : 44 : 1.18 1 : 53 : 6.07 -13 : 44 : 0.97 C 1 30 1 : 53 : 1.960 -13 : 44 : 29.26 1 : 53 : 1.96 -13 : 44 : 29.07 R 1 31 1 : 52 : 59.98 -13 : 44 : 17.92 1 : 52 : 59.98 -13 : 44 : 17.70 C 1 32 1 : 53 : 1.440 -13 : 44 : 45.92 1 : 53 : 1.44 -13 : 44 : 45.75 R 1 33 1 : 52 : 57.77 -13 : 45 : 1.92 1 : 52 : 57.77 -13 : 45 : 1.75 R 1 34 1 : 53 : 7.910 -13 : 42 : 58.53 1 : 53 : 7.92 -13 : 42 : 58.25 R 1 35 1 : 53 : 4.560 -13 : 43 : 8.05 1 : 53 : 4.56 -13 : 43 : 7.76 R 1 36 1 : 53 : 2.160 -13 : 44 : 1.81 1 : 53 : 2.17 -13 : 44 : 1.58 R 1 37 1 : 52 : 59.73 -13 : 44 : 46.74 1 : 52 : 59.73 -13 : 44 : 46.56 C 1 38 1 : 52 : 52.05 -13 : 45 : 37.23 1 : 52 : 52.04 -13 : 45 : 37.07 R 1 39 1 : 53 : 5.690 -13 : 44 : 39.07 1 : 53 : 5.70 -13 : 44 : 38.92 R 1 40 1 : 53 : 5.76 -13 : 44 : 56.02 1 : 53 : 5.77 -13 : 44 : 55.90 R 1 41 1 : 53 : 2.74 -13 : 45 : 50.56 1 : 53 : 2.74 -13 : 45 : 50.50 R 1 42 1 : 53 : 1.20 -13 : 45 : 20.00 1 : 53 : 1.20 -13 : 45 : 19.88 R 1 43 1 : 53 : 10.67 -13 : 43 : 29.85 1 : 53 : 10.69 -13 : 43 : 29.64 R 1 44 1 : 53 : 6.94 -13 : 44 : 48.83 1 : 53 : 6.95 -13 : 44 : 48.70 C 1 45 1 : 53 : 3.93 -13 : 45 : 10.01 1 : 53 : 3.93 -13 : 45 : 9.89 R 1 46 1 : 53 : 0.90 -13 : 45 : 11.87 1 : 53 : 0.90 -13 : 45 : 11.73 C 1 47 1 : 52 : 57.70 -13 : 46 : 43.19 1 : 52 : 57.69 -13 : 46 : 43.16 C 1 48 1 : 52 : 55.63 -13 : 46 : 36.20 1 : 52 : 55.62 -13 : 46 : 36.15 R 1 49 1 : 52 : 55.50 -13 : 46 : 45.48 1 : 52 : 55.49 -13 : 46 : 45.44 R 1 50 1 : 53 : 5.07 -13 : 45 : 40.71 1 : 53 : 5.07 -13 : 45 : 40.65 C 1 51 1 : 53 : 4.30 -13 : 45 : 42.29 1 : 53 : 4.30 -13 : 45 : 42.23 R 1 52 1 : 53 : 4.09 -13 : 46 : 22.49 1 : 53 : 4.09 -13 : 46 : 22.48 R 1 53 1 : 53 : 11.31 -13 : 44 : 46.55 1 : 53 : 11.32 -13 : 44 : 46.45 R 1 54 1 : 53 : 7.89 -13 : 45 : 19.96 1 : 53 : 7.90 -13 : 45 : 19.89 R 1 55 1 : 53 : 11.21 -13 : 45 : 13.82 1 : 53 : 11.22 -13 : 45 : 13.77 C 1 56 1 : 52 : 59.72 -13 : 47 : 37.00 1 : 52 : 59.71 -13 : 47 : 37.07 R 2 1 1 : 52 : 50.32 -13 : 45 : 28.76 1 : 52 : 50.31 -13 : 45 : 28.96 R 2 2 1 : 52 : 50.27 -13 : 45 : 17.72 1 : 52 : 50.26 -13 : 45 : 17.91 R 2 3 1 : 52 : 51.53 -13 : 45 : 21.01 1 : 52 : 51.52 -13 : 45 : 21.19 R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 148

Table A.3 – Astrometry Results Continued

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 2 4 1 : 52 : 41.54 -13 : 42 : 5.27 1 : 52 : 41.52 -13 : 42 : 5.26 R 2 5 1 : 52 : 52.59 -13 : 45 : 31.86 1 : 52 : 52.59 -13 : 45 : 32.05 R 2 6 1 : 52 : 49.80 -13 : 41 : 58.53 1 : 52 : 49.79 -13 : 41 : 58.48 R 2 7 1 : 52 : 57.34 -13 : 43 : 53.93 1 : 52 : 57.34 -13 : 43 : 53.99 C 2 8 1 : 52 : 56.52 -13 : 44 : 36.48 1 : 52 : 56.52 -13 : 44 : 36.59 C 2 9 1 : 52 : 49.65 -13 : 42 : 13.24 1 : 52 : 49.64 -13 : 42 : 13.21 C 2 10 1 : 52 : 50.30 -13 : 44 : 4.52 1 : 52 : 50.29 -13 : 44 : 4.62 C 2 11 1 : 52 : 56.18 -13 : 43 : 44.26 1 : 52 : 56.18 -13 : 43 : 44.31 C 2 12 1 : 52 : 57.25 -13 : 44 : 18.23 1 : 52 : 57.25 -13 : 44 : 18.32 C 2 13 1 : 52 : 56.39 -13 : 44 : 42.64 1 : 52 : 56.39 -13 : 44 : 42.76 C 2 14 1 : 52 : 46.39 -13 : 40 : 19.91 1 : 52 : 46.37 -13 : 40 : 19.75 R 2 15 1 : 52 : 46.78 -13 : 40 : 41.47 1 : 52 : 46.76 -13 : 40 : 41.34 R 2 16 1 : 52 : 44.01 -13 : 41 : 43.53 1 : 52 : 43.99 -13 : 41 : 43.48 C 2 17 1 : 52 : 46.07 -13 : 41 : 35.34 1 : 52 : 46.05 -13 : 41 : 35.28 R 2 18 1 : 52 : 49.17 -13 : 43 : 51.25 1 : 52 : 49.16 -13 : 43 : 51.34 C 2 19 1 : 52 : 51.29 -13 : 43 : 46.68 1 : 52 : 51.28 -13 : 43 : 46.75 C 2 20 1 : 52 : 52.37 -13 : 44 : 18.37 1 : 52 : 52.36 -13 : 44 : 18.48 C 2 21 1 : 52 : 55.10 -13 : 44 : 27.06 1 : 52 : 55.10 -13 : 44 : 27.17 C 2 22 1 : 52 : 53.24 -13 : 44 : 49.85 1 : 52 : 53.24 -13 : 44 : 50.00 R 2 23 1 : 52 : 54.68 -13 : 44 : 36.87 1 : 52 : 54.68 -13 : 44 : 36.99 R 2 24 1 : 52 : 56.85 -13 : 44 : 22.70 1 : 52 : 56.85 -13 : 44 : 22.80 C 2 25 1 : 52 : 56.48 -13 : 42 : 33.88 1 : 52 : 56.47 -13 : 42 : 33.84 R 2 26 1 : 52 : 57.92 -13 : 42 : 35.46 1 : 52 : 57.92 -13 : 42 : 35.43 C 2 27 1 : 52 : 58.41 -13 : 43 : 40.01 1 : 52 : 58.41 -13 : 43 : 40.05 C 2 28 1 : 53 : 0.50 -13 : 43 : 35.68 1 : 53 : 0.50 -13 : 43 : 35.71 R 2 29 1 : 53 : 0.04 -13 : 43 : 55.84 1 : 53 : 0.04 -13 : 43 : 55.89 C 2 30 1 : 52 : 50.23 -13 : 40 : 21.42 1 : 52 : 50.22 -13 : 40 : 21.26 R 2 31 1 : 52 : 49.05 -13 : 40 : 39.78 1 : 52 : 49.03 -13 : 40 : 39.64 R 2 32 1 : 52 : 50.96 -13 : 40 : 56.51 1 : 52 : 50.94 -13 : 40 : 56.38 R 2 33 1 : 52 : 55.10 -13 : 42 : 55.29 1 : 52 : 55.09 -13 : 42 : 55.29 R 2 34 1 : 52 : 57.52 -13 : 43 : 32.26 1 : 52 : 57.52 -13 : 43 : 32.29 C 2 35 1 : 52 : 59.72 -13 : 42 : 22.95 1 : 52 : 59.72 -13 : 42 : 22.89 R 2 36 1 : 53 : 0.28 -13 : 42 : 44.88 1 : 53 : 0.28 -13 : 42 : 44.84 C 2 37 1 : 52 : 51.38 -13 : 39 : 40.39 1 : 52 : 51.37 -13 : 39 : 40.17 R 2 38 1 : 52 : 50.87 -13 : 40 : 8.88 1 : 52 : 50.86 -13 : 40 : 8.69 R 2 39 1 : 52 : 56.89 -13 : 41 : 49.16 1 : 52 : 56.89 -13 : 41 : 49.07 C 2 40 1 : 53 : 1.94 -13 : 43 : 41.46 1 : 53 : 1.94 -13 : 43 : 41.48 R 2 41 1 : 52 : 53.55 -13 : 39 : 52.04 1 : 52 : 53.54 -13 : 39 : 51.82 R 2 42 1 : 52 : 53.92 -13 : 40 : 13.50 1 : 52 : 53.91 -13 : 40 : 13.30 R 2 43 1 : 53 : 1.58 -13 : 42 : 32.04 1 : 53 : 1.58 -13 : 42 : 31.99 C 2 44 1 : 52 : 56.93 -13 : 40 : 38.74 1 : 52 : 56.93 -13 : 40 : 38.56 R Continued on Next Page. . . APPENDIX A. TABLES OF DATA 149

Table A.3 – Astrometry Results Continued

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 2 45 1 : 53 : 0.93 -13 : 41 : 3.44 1 : 53 : 0.93 -13 : 41 : 3.29 R 2 46 1 : 52 : 55.70 -13 : 39 : 40.00 1 : 52 : 55.69 -13 : 39 : 39.76 C 2 47 1 : 53 : 2.90 -13 : 42 : 10.05 1 : 53 : 2.91 -13 : 42 : 9.96 C 2 48 1 : 52 : 56.88 -13 : 39 : 10.86 1 : 52 : 56.87 -13 : 39 : 10.58 R 2 49 1 : 52 : 58.86 -13 : 39 : 57.27 1 : 52 : 58.86 -13 : 39 : 57.04 R 2 50 1 : 52 : 58.75 -13 : 40 : 5.89 1 : 52 : 58.75 -13 : 40 : 5.67 R 2 51 1 : 53 : 7.14 -13 : 42 : 29.27 1 : 53 : 7.15 -13 : 42 : 29.19 C 2 52 1 : 53 : 6.86 -13 : 42 : 42.86 1 : 53 : 6.87 -13 : 42 : 42.80 R 3 1 1 : 53 : 2.01 -13 : 44 : 28.41 1 : 53 : 2.02 -13 : 44 : 28.24 R 3 2 1 : 53 : 1.49 -13 : 44 : 45.16 1 : 53 : 1.50 -13 : 44 : 45.00 R 3 3 1 : 52 : 57.83 -13 : 45 : 1.00 1 : 52 : 57.83 -13 : 45 : 0.82 R 3 4 1 : 52 : 58.89 -13 : 44 : 39.48 1 : 52 : 58.89 -13 : 44 : 39.30 C 3 5 1 : 53 : 5.73 -13 : 44 : 38.50 1 : 53 : 5.74 -13 : 44 : 38.36 R 3 6 1 : 53 : 5.79 -13 : 44 : 55.50 1 : 53 : 5.80 -13 : 44 : 55.37 R 3 7 1 : 53 : 2.77 -13 : 45 : 49.92 1 : 53 : 2.77 -13 : 45 : 49.81 R 3 8 1 : 53 : 6.26 -13 : 45 : 0.99 1 : 53 : 6.27 -13 : 45 : 0.87 C 3 9 1 : 53 : 0.82 -13 : 45 : 38.41 1 : 53 : 0.82 -13 : 45 : 38.28 R 3 10 1 : 52 : 59.48 -13 : 45 : 27.68 1 : 52 : 59.48 -13 : 45 : 27.53 C 3 11 1 : 53 : 7.69 -13 : 43 : 47.69 1 : 53 : 7.70 -13 : 43 : 47.54 R 3 12 1 : 53 : 4.99 -13 : 44 : 39.87 1 : 53 : 4.99 -13 : 44 : 39.72 C 3 13 1 : 52 : 54.37 -13 : 46 : 7.32 1 : 52 : 54.36 -13 : 46 : 7.15 R 3 14 1 : 52 : 55.54 -13 : 46 : 44.43 1 : 52 : 55.54 -13 : 46 : 44.29 R 3 15 1 : 52 : 55.44 -13 : 46 : 55.80 1 : 52 : 55.43 -13 : 46 : 55.67 R 3 16 1 : 53 : 4.33 -13 : 45 : 41.76 1 : 53 : 4.34 -13 : 45 : 41.65 R 3 17 1 : 53 : 9.87 -13 : 46 : 10.12 1 : 53 : 9.88 -13 : 46 : 10.07 C 3 18 1 : 53 : 4.11 -13 : 46 : 21.82 1 : 53 : 4.11 -13 : 46 : 21.73 R 3 19 1 : 53 : 4.45 -13 : 46 : 58.67 1 : 53 : 4.45 -13 : 46 : 58.61 R 3 20 1 : 53 : 2.60 -13 : 46 : 53.93 1 : 53 : 2.60 -13 : 46 : 53.85 R 3 21 1 : 52 : 58.41 -13 : 47 : 8.66 1 : 52 : 58.40 -13 : 47 : 8.56 C 3 22 1 : 53 : 13.94 -13 : 44 : 39.24 1 : 53 : 13.96 -13 : 44 : 39.16 R 3 23 1 : 53 : 13.50 -13 : 45 : 2.15 1 : 53 : 13.52 -13 : 45 : 2.08 R 3 24 1 : 53 : 9.72 -13 : 45 : 9.37 1 : 53 : 9.73 -13 : 45 : 9.28 R 3 25 1 : 53 : 2.94 -13 : 47 : 5.97 1 : 53 : 2.94 -13 : 47 : 5.90 C 3 26 1 : 53 : 12.71 -13 : 46 : 36.98 1 : 53 : 12.72 -13 : 46 : 36.96 R 3 27 1 : 53 : 11.24 -13 : 46 : 16.02 1 : 53 : 11.25 -13 : 46 : 15.98 C 3 28 1 : 53 : 9.38 -13 : 46 : 49.41 1 : 53 : 9.38 -13 : 46 : 49.38 C 3 29 1 : 53 : 9.44 -13 : 47 : 2.85 1 : 53 : 9.44 -13 : 47 : 2.82 C 3 30 1 : 53 : 0.12 -13 : 48 : 13.45 1 : 53 : 0.12 -13 : 48 : 13.39 R 3 31 1 : 53 : 16.37 -13 : 46 : 13.30 1 : 53 : 16.38 -13 : 46 : 13.29 C 3 32 1 : 53 : 6.71 -13 : 47 : 56.10 1 : 53 : 6.72 -13 : 47 : 56.09 R 3 33 1 : 53 : 17.51 -13 : 47 : 11.14 1 : 53 : 17.52 -13 : 47 : 11.17 C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 150

Table A.3 – Astrometry Results Continued

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 3 34 1 : 53 : 12.75 -13 : 47 : 53.21 1 : 53 : 12.76 -13 : 47 : 53.23 R 3 35 1 : 53 : 8.63 -13 : 49 : 12.39 1 : 53 : 8.63 -13 : 49 : 12.42 R 3 36 1 : 53 : 5.40 -13 : 49 : 30.19 1 : 53 : 5.40 -13 : 49 : 30.22 C 3 37 1 : 53 : 19.01 -13 : 47 : 6.61 1 : 53 : 19.03 -13 : 47 : 6.65 R 3 38 1 : 53 : 16.86 -13 : 46 : 40.94 1 : 53 : 16.87 -13 : 46 : 40.95 R 3 39 1 : 53 : 18.73 -13 : 47 : 20.75 1 : 53 : 18.74 -13 : 47 : 20.79 R 3 40 1 : 53 : 17.11 -13 : 46 : 54.70 1 : 53 : 17.12 -13 : 46 : 54.72 C 3 41 1 : 53 : 3.95 -13 : 49 : 8.37 1 : 53 : 3.95 -13 : 49 : 8.37 R 3 42 1 : 53 : 1.62 -13 : 49 : 9.72 1 : 53 : 1.61 -13 : 49 : 9.71 C 4 1 1 : 53 : 3.00 -13 : 45 : 7.73 1 : 53 : 2.99 -13 : 45 : 7.91 C 4 2 1 : 53 : 5.26 -13 : 44 : 49.10 1 : 53 : 5.25 -13 : 44 : 49.25 C 4 3 1 : 53 : 15.15 -13 : 42 : 13.17 1 : 53 : 15.15 -13 : 42 : 13.19 R 4 4 1 : 53 : 1.27 -13 : 45 : 7.53 1 : 53 : 1.25 -13 : 45 : 7.71 C 4 5 1 : 53 : 4.15 -13 : 45 : 43.56 1 : 53 : 4.14 -13 : 45 : 43.75 C 4 6 1 : 53 : 4.64 -13 : 44 : 11.94 1 : 53 : 4.63 -13 : 44 : 12.07 C 4 7 1 : 53 : 8.58 -13 : 44 : 54.45 1 : 53 : 8.57 -13 : 44 : 54.60 C 4 8 1 : 53 : 9.01 -13 : 44 : 22.05 1 : 53 : 9.00 -13 : 44 : 22.17 R 4 9 1 : 53 : 4.37 -13 : 46 : 19.25 1 : 53 : 4.36 -13 : 46 : 19.47 C 4 10 1 : 53 : 3.06 -13 : 45 : 46.86 1 : 53 : 3.05 -13 : 45 : 47.06 C 4 11 1 : 53 : 4.54 -13 : 46 : 5.01 1 : 53 : 4.53 -13 : 46 : 5.21 R 4 12 1 : 53 : 3.06 -13 : 45 : 11.99 1 : 53 : 3.05 -13 : 45 : 12.17 R 4 13 1 : 53 : 8.46 -13 : 45 : 8.95 1 : 53 : 8.45 -13 : 45 : 9.11 R 4 14 1 : 53 : 11.49 -13 : 45 : 10.74 1 : 53 : 11.49 -13 : 45 : 10.88 C 4 15 1 : 53 : 15.50 -13 : 43 : 59.74 1 : 53 : 15.50 -13 : 43 : 59.83 R 4 16 1 : 53 : 12.97 -13 : 43 : 0.82 1 : 53 : 12.97 -13 : 43 : 0.88 R 4 17 1 : 53 : 14.40 -13 : 43 : 25.26 1 : 53 : 14.40 -13 : 43 : 25.33 R 4 18 1 : 53 : 14.34 -13 : 43 : 3.96 1 : 53 : 14.34 -13 : 43 : 4.02 R 4 19 1 : 53 : 18.65 -13 : 42 : 56.52 1 : 53 : 18.65 -13 : 42 : 56.55 R 4 20 1 : 53 : 0.79 -13 : 44 : 45.24 1 : 53 : 0.78 -13 : 44 : 45.41 C 4 21 1 : 53 : 2.90 -13 : 44 : 26.73 1 : 53 : 2.88 -13 : 44 : 26.87 C 4 22 1 : 53 : 12.93 -13 : 42 : 15.14 1 : 53 : 12.93 -13 : 42 : 15.16 R 4 23 1 : 52 : 56.77 -13 : 44 : 26.60 1 : 52 : 56.75 -13 : 44 : 26.77 C 4 24 1 : 52 : 57.53 -13 : 44 : 8.59 1 : 52 : 57.51 -13 : 44 : 8.74 C 4 25 1 : 52 : 59.99 -13 : 44 : 28.27 1 : 52 : 59.97 -13 : 44 : 28.43 C 4 26 1 : 53 : 0.86 -13 : 44 : 2.23 1 : 53 : 0.84 -13 : 44 : 2.37 C 4 27 1 : 53 : 0.33 -13 : 43 : 51.68 1 : 53 : 0.31 -13 : 43 : 51.82 R 4 28 1 : 52 : 59.29 -13 : 44 : 44.10 1 : 52 : 59.27 -13 : 44 : 44.27 C 4 29 1 : 52 : 59.77 -13 : 44 : 22.73 1 : 52 : 59.75 -13 : 44 : 22.88 C 4 30 1 : 53 : 2.49 -13 : 43 : 34.40 1 : 53 : 2.48 -13 : 43 : 34.51 C 4 31 1 : 53 : 8.39 -13 : 42 : 3.17 1 : 53 : 8.38 -13 : 42 : 3.20 R 4 32 1 : 53 : 10.81 -13 : 42 : 23.96 1 : 53 : 10.80 -13 : 42 : 24.00 C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 151

Table A.3 – Astrometry Results Continued

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 4 33 1 : 52 : 55.96 -13 : 43 : 53.93 1 : 52 : 55.93 -13 : 43 : 54.08 C 4 34 1 : 52 : 56.74 -13 : 43 : 51.08 1 : 52 : 56.72 -13 : 43 : 51.23 C 4 35 1 : 52 : 57.49 -13 : 43 : 52.55 1 : 52 : 57.47 -13 : 43 : 52.69 C 4 36 1 : 53 : 8.20 -13 : 41 : 36.66 1 : 53 : 8.19 -13 : 41 : 36.68 C 4 37 1 : 52 : 56.85 -13 : 44 : 9.47 1 : 52 : 56.83 -13 : 44 : 9.63 C 4 38 1 : 52 : 56.97 -13 : 43 : 26.54 1 : 52 : 56.95 -13 : 43 : 26.67 C 4 39 1 : 52 : 57.87 -13 : 43 : 22.74 1 : 52 : 57.85 -13 : 43 : 22.86 C 4 40 1 : 53 : 7.11 -13 : 41 : 20.70 1 : 53 : 7.09 -13 : 41 : 20.71 R 4 41 1 : 53 : 8.85 -13 : 40 : 37.06 1 : 53 : 8.84 -13 : 40 : 37.03 R 4 42 1 : 52 : 58.45 -13 : 42 : 40.92 1 : 52 : 58.43 -13 : 42 : 41.02 C 4 43 1 : 53 : 2.59 -13 : 40 : 31.44 1 : 53 : 2.57 -13 : 40 : 31.4 4 R 4 44 1 : 52 : 56.53 -13 : 42 : 41.99 1 : 52 : 56.50 -13 : 42 : 42.09 C 4 45 1 : 52 : 56.89 -13 : 42 : 24.21 1 : 52 : 56.87 -13 : 42 : 24.30 R 4 46 1 : 52 : 56.51 -13 : 42 : 4.78 1 : 52 : 56.48 -13 : 42 : 4.85 C 4 47 1 : 52 : 58.48 -13 : 41 : 56.73 1 : 52 : 58.46 -13 : 41 : 56.79 C 4 48 1 : 53 : 1.47 -13 : 40 : 54.99 1 : 53 : 1.45 -13 : 40 : 55.01 C 4 49 1 : 53 : 7.70 -13 : 40 : 23.36 1 : 53 : 7.69 -13 : 40 : 23.33 R 4 50 1 : 53 : 6.60 -13 : 40 : 4.44 1 : 53 : 6.58 -13 : 40 : 4.40 R 5 1 1 : 52 : 45.48 -13 : 43 : 38.93 1 : 52 : 45.49 -13 : 43 : 39.00 R 5 2 1 : 52 : 44.48 -13 : 43 : 38.79 1 : 52 : 44.49 -13 : 43 : 38.87 R 5 3 1 : 52 : 44.16 -13 : 46 : 43.47 1 : 52 : 44.16 -13 : 46 : 43.43 C 5 4 1 : 52 : 45.14 -13 : 46 : 43.60 1 : 52 : 45.15 -13 : 46 : 43.56 R 5 5 1 : 52 : 48.12 -13 : 46 : 41.59 1 : 52 : 48.12 -13 : 46 : 41.55 C 5 6 1 : 52 : 47.50 -13 : 43 : 24.59 1 : 52 : 47.50 -13 : 43 : 24.67 R 5 7 1 : 52 : 51.09 -13 : 44 : 35.70 1 : 52 : 51.10 -13 : 44 : 35.73 R 5 8 1 : 52 : 52.46 -13 : 45 : 3.23 1 : 52 : 52.47 -13 : 45 : 3.24 C 5 9 1 : 52 : 52.10 -13 : 45 : 23.44 1 : 52 : 52.10 -13 : 45 : 23.43 R 5 10 1 : 52 : 52.86 -13 : 46 : 4.70 1 : 52 : 52.87 -13 : 46 : 4.67 C 5 11 1 : 52 : 51.55 -13 : 46 : 41.51 1 : 52 : 51.55 -13 : 46 : 41.46 C 5 12 1 : 52 : 51.69 -13 : 43 : 17.16 1 : 52 : 51.69 -13 : 43 : 17.24 R 5 13 1 : 52 : 50.76 -13 : 43 : 20.67 1 : 52 : 50.76 -13 : 43 : 20.75 R 5 14 1 : 52 : 49.75 -13 : 43 : 46.77 1 : 52 : 49.75 -13 : 43 : 46.83 R 5 15 1 : 52 : 49.86 -13 : 47 : 13.48 1 : 52 : 49.86 -13 : 47 : 13.42 R 5 16 1 : 52 : 48.66 -13 : 47 : 35.78 1 : 52 : 48.67 -13 : 47 : 35.70 R 5 17 1 : 52 : 59.48 -13 : 43 : 48.68 1 : 52 : 59.48 -13 : 43 : 48.73 C 5 18 1 : 52 : 57.93 -13 : 43 : 51.50 1 : 52 : 57.93 -13 : 43 : 51.55 C 5 19 1 : 53 : 0.14 -13 : 43 : 52.73 1 : 53 : 0.14 -13 : 43 : 52.77 C 5 20 1 : 52 : 58.60 -13 : 44 : 20.98 1 : 52 : 58.60 -13 : 44 : 21.01 C 5 21 1 : 52 : 57.08 -13 : 43 : 54.94 1 : 52 : 57.08 -13 : 43 : 54.99 C 5 22 1 : 52 : 56.56 -13 : 44 : 25.89 1 : 52 : 56.56 -13 : 44 : 25.92 C 5 23 1 : 52 : 55.76 -13 : 45 : 30.69 1 : 52 : 55.76 -13 : 45 : 30.68 C Continued on Next Page. . . APPENDIX A. TABLES OF DATA 152

Table A.3 – Astrometry Results Continued

Field Ap RA (g-band) Dec (g-band) RA (i-band) Dec (i-band) Comments (h:m:s) (h:m:s) (h:m:s) (h:m:s) 5 24 1 : 52 : 56.19 -13 : 45 : 31.61 1 : 52 : 56.19 -13 : 45 : 31.60 R 5 25 1 : 52 : 57.40 -13 : 44 : 20.07 1 : 52 : 57.40 -13 : 44 : 20.10 C 5 26 1 : 52 : 55.64 -13 : 44 : 24.00 1 : 52 : 55.64 -13 : 44 : 24.04 R 5 27 1 : 52 : 55.49 -13 : 45 : 34.33 1 : 52 : 55.49 -13 : 45 : 34.31 R 5 28 1 : 52 : 58.81 -13 : 47 : 10.15 1 : 52 : 58.81 -13 : 47 : 10.08 C 5 29 1 : 52 : 53.86 -13 : 47 : 27.91 1 : 52 : 53.86 -13 : 47 : 27.83 R 5 30 1 : 52 : 58.04 -13 : 47 : 37.25 1 : 52 : 58.04 -13 : 47 : 37.16 R 5 31 1 : 52 : 57.04 -13 : 47 : 37.11 1 : 52 : 57.04 -13 : 47 : 37.02 R 5 32 1 : 53 : 0.93 -13 : 43 : 46.25 1 : 53 : 0.92 -13 : 43 : 46.30 C 5 33 1 : 53 : 1.90 -13 : 44 : 23.87 1 : 53 : 1.90 -13 : 44 : 23.90 C 5 34 1 : 53 : 1.44 -13 : 44 : 50.29 1 : 53 : 1.44 -13 : 44 : 50.30 C 5 35 1 : 53 : 0.61 -13 : 46 : 47.61 1 : 53 : 0.60 -13 : 46 : 47.55 R 5 36 1 : 53 : 2.58 -13 : 43 : 47.48 1 : 53 : 2.58 -13 : 43 : 47.52 R 5 37 1 : 53 : 2.45 -13 : 47 : 2.80 1 : 53 : 2.45 -13 : 47 : 2.72 C 5 38 1 : 53 : 4.96 -13 : 47 : 3.06 1 : 53 : 4.96 -13 : 47 : 2.98 R 5 39 1 : 53 : 3.83 -13 : 44 : 53.85 1 : 53 : 3.82 -13 : 44 : 53.85 C 5 40 1 : 53 : 4.42 -13 : 45 : 50.45 1 : 53 : 4.42 -13 : 45 : 50.42 R 5 41 1 : 53 : 5.06 -13 : 42 : 57.80 1 : 53 : 5.06 -13 : 42 : 57.87 R