The system of NGC 4526

Leonie Chevalier

Presented in fulfillment of the requirements of the degree of Masters by Research

–

Faculty of Science and Technology Swinburne University

i

You couldn’t put off the inevitable. Because sooner or later, you reached the place when the inevitable just went and waited - Terry Pratchett, Small Gods ii Abstract

With ages up to ∼ 12.8 Gyr, globular clusters (GCs) are some of the oldest objects we observe in and are thought to preserve their parent ’s chemo-dynamical properties at the time of their formation. A single galaxy can have tens of thousands of GCs associated with it. Globular cluster systems have long been thought to be a useful tool for constraining galaxy formation scenarios and even to infer a galaxy’s individual formation history. More recently GCs have been used to infer dark matter masses of galaxies. This thesis focuses on the GC system of the NGC 4526. We used Subaru imaging to study the system’s full extent and infer the total number of GCs as well as the substructure in the GC system. Additionally we used the DEIMOS spectrograph on the Keck telescope to obtain radial velocities for 106 GCs to be used in studying the dark matter content of NGC 4526. The combination of multiband photometric data in combination with the spectroscopic data gained by DEIMOS is a significant improvement on existing data sets that only partially imaged the GC system. To put our findings into a broader context we compared NGC 4526 to the results of 26 other elliptical and lenticular galaxies (and their GC systems) from existing literature. Additionally we investigated the dark matter content of NGC 4526 and how this measurement is dependent on sample selection and model assumptions. We found that this galaxy has an unusually high fraction (80%) of metal-poor GCs and a high dark matter fraction for its mass. In fact it was one of the highest metal poor fractions observed in our sample of 27 galaxies. However, it is still consistent with the general trend we derived from our full sample of galaxies. The GC system itself did not appear overly extended. We propose that this particular galaxy most likely formed through a series of minor mergers. iii iv Acknowledgements

I would like to thank the people who have supported me throughout writing this thesis. To Vincent for his love and support throughout, to Manodeep and Ned for the best coding help and high fives and most of all to Caitlin without whom this thesis would never have been finished. v vi Declaration

The work presented in this thesis has been carried out in the Centre for Astrophysics & Supercomputing at Swinburne University of Technology between 2016 and 2018. This thesis contains no material that has been accepted for the award of any other degree or diploma. To the best of my knowledge, this thesis contains no material previously published or written by another author, except where due reference is made in the text of the thesis.

Leonie Chevalier Melbourne, Victoria, Australia 2018 vii

Contents

Abstract ii

Acknowledgements iii

Declaration v

List of Figures x

List of Tables xi

1 Introduction 1 1.1 Individual globular clusters ...... 1 1.2 Globular cluster systems ...... 2 1.3 Globular cluster systems and their host Galaxies ...... 3 1.4 Large scale studies of globular cluster systems ...... 5 1.4.1 ACS and Cluster Survey ...... 5 1.4.2 Next Generation survey ...... 5 1.4.3 SLUGGS ...... 6 1.5 The globular cluster system - dark matter connection ...... 8 1.6 The lenticular galaxy NGC 4526 ...... 8

2 Simulations 11 2.1 Simulations of globular cluster system formation and evolution ...... 11 2.1.1 Globular cluster systems in galaxy formation models ...... 11 2.1.2 Globular clusters in cosmological simulations ...... 14

3 Observations 21 3.1 Data Collection and Reduction ...... 21

4 Photometry 23 4.1 Galaxy light model ...... 23 4.2 Object detection ...... 24 4.3 Zeropoint estimates and final magnitudes ...... 24

ix x Contents

4.4 Globular cluster candidate selection ...... 25 4.5 Globular cluster colour distribution ...... 31 4.6 HST sources ...... 32 4.7 Completeness ...... 33

5 Analysis 37 5.1 Radial colour gradients ...... 37 5.2 Surface density profiles ...... 38

6 Global relations of GC systems 43 6.1 Radial extent of GC system ...... 43 6.2 Mass and dark matter fraction ...... 44 6.3 Specific frequency ...... 49

7 Discussion 53

8 Conclusion and proposed further work 59 8.1 Proposed further work ...... 60

Bibliography 70

A Appendix A 71 List of Figures

4.1 Colour-magnitude diagram ...... 26 4.2 Colour-colour diagram ...... 27 4.3 CLASS selection ...... 29 4.4 GC candidate positions ...... 30 4.5 Histogram of GC candidates ...... 32 4.6 Completeness tests ...... 34

5.1 Colour Gradient ...... 38 5.2 Surface density profiles ...... 40

6.1 Comparison of NGC 4526 to other lenticular and elliptical galaxies . . . . . 45 6.2 Comparison of GCs extent to Host galaxy properties ...... 46 6.3 Radial velocities of NGC 4526 GCs ...... 48 6.4 Dark matter fraction scaling relation ...... 50

xi

List of Tables

4.1 GMM results ...... 33

5.1 Sersic fits ...... 41

6.1 Dark matter fractions ...... 51

A.1 Full candidate list ...... 71

xiii

1 Introduction

1.1 Individual globular clusters

Globular clusters (GC) are gravitationally bound clusters of ∼ 104 - 106 situated in and around galaxies. They are spherical with an average half light radius of 3pc (Masters 4 −3 et al., 2010) and have an average core density of ∼ 8×10 M pc Portegies Zwart et al. (2010). With ages up to ∼ 12.8 Gyr (Forbes et al., 2015) globular clusters are among the oldest objects we observe in galaxies. For a long time astronomers believed that all stars within a globular cluster formed at the same time and therefore are an example of a simple stellar population. This would imply that they have the potential to preserve their parent galaxy’s chemo-dynamical properties at the time of their formation, and therefore they make an ideal tracer for galaxy formation and evolution. Conditions that promote the formation of globular clusters are gas rich, high density and high temperature, which makes galaxies with gas-rich clumpy discs or those that are undergoing major mergers almost ideal for GCs formation. It has also been proposed that GCs may form within their own low-mass dark matter (DM) halos (Kimm et al., 2016). Initial issues with this model have been that current observations show no evidence of DM halos associated with GCs (Conroy et al., 2011). Furthermore, based on our current understanding of cosmology and star formation, had GCs indeed formed in a DM halo that was subsequently stripped, we would expect to see a range of stellar populations within the GCs due to the continuous accretion of gas onto the DM halo.

1 2 Chapter 1. Introduction

Whether or not globular clusters are single stellar populations has been debated for some time. To determine the presence of multiple populations one can plot the stars in a GC on a Hertzsprung-Russell diagram, which would indicate the main sequence of the individual GCs in the GC system as well as any turnoff points that would indicate the age of the stellar population. Most GCs we observe do not contain any gas that could trigger renewed star formation.

The number of GCs with multiple stellar populations (MSP) is growing (Wang et al., 2017) as technology is improving. We now find MSP in most massive GCs. We also have compelling evidence for MSPs in most of the galactic GCs (Bastian & Lardo, 2017). One example of a GC with MSP is ω Centauri, which is the most massive GC. The stars in ω Centauri were found to have a large spread of metallicities by Freeman & Rodgers (1975); Freeman & Norris (1981). Additional evidence was found in the form of multiple main sequence turnoffs (Sollima et al., 2005; Piotto, 2006) and multiple red giant branches (Lee et al., 1999; Pancino et al., 2000). All together this is compelling evidence for MSPs which are several Gyrs apart in age. It has been suggested by several studies (Zinnecker et al., 1988; Freeman, 1993; Gnedin et al., 2002) that ω Centauri is not what would classically be called a GC but instead is an ultra-compact dwarf galaxy, the stripped core of a galaxy that had in the past been acquired by the host galaxy (i.e the Milky Way). Regardless of the nature of omega centauri it is apparent that the view of GCs as simple stellar population systems is outdated. This could potentially change on how we use GC systems of tracers of galaxy formation histories.

1.2 Globular cluster systems

A single galaxy can have tens of thousands of GCs associated with it. When talking about not a single but a multitude of GCs associated with the same galaxy, we refer to it as a GC system. Globular cluster systems have a long history of being used to constrain galaxy assembly histories (Ashman & Zepf, 1992; West et al., 2004; Brodie, 2009) and have further uses in inferring global properties of galaxies such as their mass. Their attributes such as individual GC radial velocities, spatial distribution and the presence of subpopulations (in the system not in individual GCs) are closely linked to their host galaxy’s formation 1.3. Globular cluster systems and their host Galaxies 3 history. Not only are globular clusters observed in galaxies of all morphological types (Brodie & Strader, 2006; Harris et al., 2013), they offer the advantage of extending past the diffuse galaxy light, allowing the galaxy’s potential to be probed out to galactic radii of ≥ 10Re (where Re refers to the effective radius of the host galaxy). Photometric analysis allows a galaxy’s globular cluster system to be separated by colour/metallicity into blue/metal-poor globular clusters and red/metal-rich globular clusters Brodie et al. (2014). Usher et al. (2012) confirmed the relationship between GC colours and GC metallicities and therefore we assume a direct relation between a GCs colour and metallicity. The presence of bimodal subpopulations indicates an underlying difference in the formation/acquisition of each subpopulation. In early-type galaxies, we observe that red GCs are generally more centrally concentrated and do not reach the spatial extent of blue GCs (Harris, 2010). Moreover, it is suggested and observed that red GC kinematics mirror stellar kinematics (Peng et al., 2003; Faifer et al., 2011; Strader et al., 2011; Pota et al., 2013a; Li et al., 2015). This is not the case for the blue subpopulation whose spacial distribution is generally associated with the galaxy’s halo population (Harris, 2010). All of the features of GC systems just described offer avenues of testing for a variety of galaxy formation scenarios. Theoretical scenarios of the formation of a bimodal globular cluster system include models of major mergers, hierarchical mergers, multiphase collapse and two-phase formation (Ashman & Zepf, 1992; Forbes et al., 1997, 2011; Brodie et al., 2014). These four formation scenarios are not mutually exclusive and do overlap to a certain degree; for historic reasons we will discuss them separately in the sections below.

1.3 Globular cluster systems and their host Galaxies

One of the earliest instances of GCs being used to infer information about their host galaxy came from Shapley (1918), who used them to determine the structure of the Milky Way (MW) as well as the location of our within it. This led to the further discovery of two distinct groups of GCs, namely halo and bulge GCs. Over the years a large amount of information on the MW GC system has been a amassed by scientists, most recently in the form of the WAGGS survey (Usher et al., 2017). WAGGS is a library of integrated spectra of 64 globular clusters in the Milky Way, 14 in the Large Magellanic Cloud, 5 4 Chapter 1. Introduction in the Small Magellanic Cloud and 3 in the spheroidal. Apart from the MW, one of the best studied late-type galaxies is Andromeda, which we know to contain ∼450 GCs (Barmby et al., 2000; Perrett et al., 2002). However there are many fewer studies of the GC systems for late-type galaxies than early-type galaxies. As an example in the catalogue of 82 galaxies (whose GC systems had been studied by Ashman & Zepf (1998)) only 12 were classified as spirals. Rhode (2003)(and references therein) suggest that this may be due to the fact that the intrinsic structure and line of sight extinction of spiral galaxies makes it difficult to study their GC systems unless the galaxy is edge on. They further pointed out that when extrapolating total number of GCs contained in spiral galaxies from central HST imaging (as it is commonly done with early-type galaxies) the calculated value is off by 20% -75%. Early-type galaxies (ETGs) have some of the largest most complex GC systems observed (Peng et al., 2008). Elliptical galaxies have some of the highest number of GCs per unit luminosity (measurement also known as specific frequency), which makes GCs ideal to retrace an ’s formation history. The number of subpopulations, their spatial distribution and how GC kinematics relate to that of field stars are indicators for possible formation mechanisms. From studies such as Kim et al. (2013) (who observed NGC 1387, NGC 1399 and NGC 1404 and their GC systems), it was determined that red GCs are generally located closer to the centre of the galaxy and that their surface density rapidly declines with galactocentric radius, whereas the surface density of blue globular clusters declines much less rapidly.

Elliptical galaxies are predominately found in galaxy clusters and are less common in isolated environments. Caso et al. (2013) studied the (mostly) isolated elliptical galaxy NGC 7507. Unlike the aforementioned giant elliptical galaxies, this galaxy displays a low specific frequency and a trimodal rather than a bimodal GC system. The third (intermediate) subpopulation was attributed to a past starburst event as the colour profile of that GC subpopulation was strongly peaked. The environment can have a strong impact on a galaxy’s GC system. Elliptical galaxies in cluster environments have some of the highest measured specific frequencies followed by lenticular galaxies. As presented in Kartha et al. (2016), there is no link between a galaxies environment and specific frequency. But, as found in Kartha et al. (2014), the higher the environmental density of an early type galaxy the lower its blue to red GC ratio will be. In a study of NGC 1.4. Large scale studies of globular cluster systems 5

821 (Spitler et al., 2008), it is argued that the observed trends of GC system properties associated with environment are largely a product of sample bias. Once the underlying trends associated with galaxy mass are removed, the trends associated with environment are no longer present. At this moment in time evidence points us to believe that there is little if no correlation of the galaxies environment with the number of associated GCs.

1.4 Large scale studies of globular cluster systems

1.4.1 ACS Virgo and Survey

The ACS Virgo and Fornax Cluster Survey has to this day taken data on over 100 early- type galaxies in the Virgo cluster and 43 early-type galaxies in the Fornax cluster. Obser- vations were carried out using the Advanced Camera for Surveys mounted on the , with a field of view of 202 arcsec × 202 arcsec. (Jordan et al., 2007) states that the filters F475W and F850LP (g and z in Sloan filters) were chosen because of their high throughput. A signal to noise of > 10 was achieved for magnitudes g≤25.5 mag and z≤23.5 mag. For regions with little background this limit could be pushed to g≤26.1 mag and z≤24.8 mag (Jordan et al., 2007). This large-scale imaging survey resulted in the publication of over 10000 GC candidates and their properties by Jord´anet al. (2009). This large-scale homogeneous study of GC systems and their host galaxies allowed for exploration of how the fundamental properties of GC systems are related to their host galaxy luminosity (Jordan et al., 2004; Jord´anet al., 2006; Peng et al., 2008). Addition- ally, Peng et al. (2008) confirmed that for Virgo cluster galaxies the specific frequency decreased with the galaxy’s distance from M87.

1.4.2 Next Generation Virgo Cluster survey

The Next Generation Virgo Cluster survey was completed in 2012. It covers a 104 deg field centered around M87 and M49. Imaging was obtained using the Canada France Hawaii Telescope (CFHT) in u, g, r, i and z bands. It also reached a signal to noise of 10 down to magnitudes of u = 25.9 mag, g = 25.7 mag, r = 25.2 mag , i = 24.9 mag and z = 24.6 mag. While this entire survey was not designed purely to look at GCs, there are still several 6 Chapter 1. Introduction exciting science outcomes to be reported. For one Durrell et al. (2014) reported that ∼ 35 % of the GCs found were associated with M87 or M49. Additionally, for both of these galaxies, they confirmed that the red GC distribution follows the galaxy light and does not extend as far as the blue subpopulation. Based on their results they proposed that the blue subpopulation was largely accreted from dwarf galaxies and makes up most of the intracluster (GCs associated with the cluster environment rather than any one particular galaxy) GC population. Powalka et al. (2016) attempted to measure the ages and metallicities for 1843 GCs in the NGVS sample using simple stellar populations models applied to the photometry. Most of their GCs were assigned ages below 9 Gyrs, however, they expressed concern about the ages output when using the GC colour and standard population synthesis model com- parison. They finally concluded that we cannot yet use photometric ages and metallicities of GCs to uncover detailed formation histories of individual galaxies. These large scale GC system surveys focus purely on galaxies in a cluster environment.

1.4.3 SLUGGS

When aiming to study the formation of GCs systems one has to also include field galax- ies and compare their GC systems to those found in cluster galaxies. The survey that aimed to adress this by studying GC systems of galaxies in a variety of environments is the SLUGGS survey. The SAGES Legacy Unifying Globulars and GalaxieS (SLUGGS) survey combines both photometric and spectral data for a large range of early-type galax- ies (ETGs). Understanding the photometric and kinematic properties of globular clusters opens a different avenue for the study of galaxy assembly. The sample consists of 25 galaxies spanning a distance range of 9-27 Mpc and a luminosity range of MK =-22.3 to

MK =-25.5 mag. With its large radial coverage (generally out to ∼ 8Re, and to ∼15Re in some cases.), this survey was designed to probe the formation histories, dark matter con- tent and chemo-dynamical properties of ETGs. Multi-band photometric observations were carried out using the Subaru/Suprime-Cam instrument (field of view 34 x 27 arcmin2). Using this data SLUGGS is able to separate globular clusters into subpopulations based on their colour (blue/red) and, as demonstrated in Spitler & Forbes (2009), Hudson et al. (2014) and Harris et al. (2014), calculate the total galaxy mass based on GC numbers. 1.4. Large scale studies of globular cluster systems 7

This is due to the scaling relation that exists between the total globular cluster system mass and the total mass of the galaxy. Spectroscopic observations were performed with the DEep Imaging Multi-Object Spec- trograph (DEIMOS, Faber et al., 2003) mounted on the 10m Keck-II telescope. A different number of slitmasks were used for each galaxy depending on its number of globular cluster candidates. Objects thought to be globular clusters on the Subaru images were selected for spectroscopic observations. This was determined by assigning them a priority ranking based on their colour, magnitude and position in the galaxy. The large collecting area of the 10-meter Keck primary mirror combined with the 16 × 5 arcmin2 of DEIMOS makes it the ideal configuration to investigate the outskirts of galaxies through their glob- ular cluster subpopulations. For all observations, DEIMOS was set up with the 1200 l/m grating centered on 7800 A˚ together with 1 arcsec wide slits, allowing coverage of the region between 6500 and 8700 A˚ with a resolution of ∆ λ ∼ 1.5 A˚. The online pipeline DEIMOS/spec2d is used to reduce the raw spectra. The pipeline outputs individual spec- tra for each of the masks’ slits, which have been calibrated and sky-subtracted (Cooper et al., 2012; Newman et al., 2013). Globular cluster radial velocities are calculated by mea- suring the Doppler shift of Calcium Triplet (CaT) absorption lines. CaT lines are found in the near-infrared part of the globular cluster’s spectrum at 8498 A˚, 8542 A˚ and 8662 A˚ respectively. From the radial velocities, we can deduce globular cluster system properties such as velocity dispersion and kinematic position angle (Foster et al., 2011; Pota et al., 2013b). Globular cluster kinematics can also be used to calculate galaxy masses, which was successfully demonstrated in Alabi et al. (2016) (see Section 1.5). Observations of globular cluster systems in nearby galaxies (including but not limited to the SLUGGS survey) have found significant differences in the properties of blue GCs and red GCs. A study by Forbes et al. (2015), which investigated the globular cluster systems in 11 early-type galaxies, found distinct metal-poor and metal-rich subpopulations. These subpopulations had metallicity ranges of -1.34 [Fe/H] to -1.05 [Fe/H] and -0.47 [Fe/H] to 0.6 [Fe/H] for blue GCs and red GCs respectively. From the measured metallicities it is possible to estimate the age of these globular cluster systems, leading to mean ages of 12.5 Gyr (blue GCs) and 11.5 Gyr (red GCs). Additionally, red GCs are observed to be more centrally concentrated than blue GCs, which occupy the outer regions of their host 8 Chapter 1. Introduction galaxy. Large-scale kinematic studies of globular cluster systems are still relatively rare; however studies by Richtler et al. (2014) (NGC 1316) and Cote et al. (2003) (NGC 4472) both observed that blue GCs have higher velocity dispersions (∼290 kms−1 and ∼340 kms−1 compared to ∼255 kms−1 and ∼265 kms−1 in blue GCs and red GCs respectively). These findings are further supported by the study of Pota et al. (2013b) of twelve galaxies from the SLUGGS sample. Adiitionally, Dowell et al. (2014) produced 51 measurements and combined them with literature values to construct a database of velocities for 360 GCs in N4594. Dowell et al. (2014) likewise found that the blue GCs have higher velocity dispersions than the red GCs, at least inside a radius of ∼6 Re.

1.5 The globular cluster system - dark matter connection

Alabi et al. (2017, 2016) used the measurements of GCs within 5 Re to calculate the total (stellar + dark matter) mass of galaxies by using them as tracer mass estimators. Tracer mass estimators offer a way to estimate the enclosed mass from the projected positions and line-of-sight velocities of a tracer population (Evans et al., 2003). Having an estimate of the stellar mass of the galaxy, it is then possible to infer the dark matter mass and dark matter fraction (fDM ) of the galaxy. Forbes et al. (2016) applied this method to a number of SLUGGS galaxies. Their results indicated that just using the blue (i.e metal-poor) subpopulation of a GC system would be a better indicator to study a galaxy’s dark matter content than using only the red subpopulation. Out of the 16 galaxies they studied, they only found two outliers from this relation: NGC 4494, which has been shown to contain a tri-modal GC system (Foster et al., 2011), and NGC 4526.

1.6 The lenticular galaxy NGC 4526

This particular galaxy was of special interest to us because of a previous study undertaken by Forbes et al. (2016) examining the relation between the fraction of blue globular clusters and its host galaxy’s dark matter fraction. In Forbes et al. (2016), NGC 4526 does not fall onto the empirical relation between blue globular cluster and dark matter fractions. There could be several reasons for this: the dark matter mass or the fraction of blue globular clusters may have been over- or under-estimated. We deem a misestimation in the fraction 1.6. The lenticular galaxy NGC 4526 9 of blue GCs is more likely as it was calculated from HST imaging which only captured the central galaxy regions and while Jord´anet al. (2009) did attempt to correct for the missing globular clusters, their result may well not be a wholly accurate description of the whole GC system. The Subaru imaging of NGC 4526 from SLUGGS has a much larger field of view and will be able to observe the full extent of the GC system associated with NGC 4526. The other outlier, NGC 4494, is an unusual system as it has been found to contain a tri-modal system and having possibly experienced an interaction event in its recent past (Foster et al., 2011). The presence of a non-bimodal system would make it difficult if not impossible to predict an accurate blue fraction of GCs. It is therefore even more important for us to study the full extent of NGC 4526’s GC system as this third population could well only be present at large galactocentric distances not covered by HST imaging.

2 Simulations

2.1 Simulations of globular cluster system formation and evolu- tion

2.1.1 Globular cluster systems in galaxy formation models

Models of major and hierarchical mergers

Galaxies assemble their mass via mergers, both major and minor. To be classed as a major merger, the galaxy has to merge with another galaxy of at least half its own mass though the exact definition will vary between studies. As long as there is sufficient gas in the system, galaxies undergoing a major merger will experience a new burst of star formation, which we believe to include the formation of new GCs. Newly formed GCs will display distinctly different metallicities, as they are likely to form from a more chemically enriched interstellar medium (ISM). Pre-existing GCs can be disrupted by the merger event and be displaced from their original position in the galaxy (e.g migrating to the halo) (Kruijssen, 2016). The theory that major parts of a GC systems could form by major mergers was put forward by Ashman & Zepf (1992). In the model of hierarchical formation, massive objects such as M87 are predicted to have formed from a vast number of mergers involving less massive galaxies. And even the less massive galaxies originally formed through the merger of smaller ones. Blue, metal-poor GCs form first in the disk of the host galaxy at high (z ∼ 10 − 15) (Brodie & Strader, 2006). Newly

11 12 Chapter 2. Simulations formed GCs begin their lives in the high-density environment of the disc, where they are subjected to interactions and disruptions (such as tidal shock) potentially leading to the destruction of individual GCs. Such processes are more likely to affect and disrupt low- mass GCs (Kruijssen, 2014). Subsequent galaxy mergers create a favourable environment for GC formation and therefore increase the galaxy’s GC formation efficiency. Galaxy mergers allow GCs to migrate from the hostile disc environment to the galactic halo Kruijssen (2014). For a merger event to potentially ‘free’ a GC from the disc, the GC’s host galaxy has to undergo a major merger. The GCs of the less massive galaxy will be acquired by the other through tidal stripping, accreting them into the more massive galaxy’s halo (Kruijssen, 2014). Galaxy mergers containing large quantities of gas (‘wet’ mergers) will result in a new burst of star (and GC) formation. Nevertheless, a merger event does not immediately imply GCs will migrate into the halo (then carry on aging almost unperturbed) but is very much dependent on whether the GC was pre-existing in the galaxy or not. If the GC has formed before the peak interaction between the two galaxies, the GC is more likely to survive (Kruijssen, 2014). Since galaxies grow more massive with each merger the frequency of major mergers reduces. Consequently fewer GCs migrate into the halo. The merger-induced GCs should be more metal-rich than GCs that formed in-situ to the progenitor galaxies. GCs forming during galaxy mergers would make up the red GC subpopulations observed in galaxies today. Simulations described in Bekki et al. (2005) predict, in line with the major merger scenario, that the higher velocity dispersions in blue GCs are a result of angular momentum transfer during mergers. Such angular motion transfers would naturally lead to a more spatially extended GC system. Additional simulations such as Tonini (2013a); Li & Gnedin (2014a) explore this method of GC formation. We will discuss some simulations dealing with major merger scenarios and GC formation in Section 2.1.2.

Multiphase Collapse

Alternatively to the merger scenarios described above, Forbes et al. (1997) proposed an another formation process of GCs. This theory links GC formation and GC proportions to the several distinct phases of collapse in the formation of galaxies. These phases are: 2.1. Simulations of globular cluster system formation and evolution 13

• Pre-galaxy phase: In this first stage, GCs form from dense, gravitationally bound clouds of gas undergoing frequent chaotic mergers. Even though there is an abun- dance of gas and rapid star formation, only a small fraction of the gas will be converted into stars. Key features of such early GCs is low metallicity, high-velocity dispersion and random distribution throughout the entire volume of the protogalac- tic cloud. It is noteworthy that at the formation epoch, the overall ratio of stars being bound in GCs is larger than the number of field stars, leading to a very high specific frequency (Forbes et al., 1997).

• Galaxy phase: The infant galaxy undergoes a further collapse leading to a renewed burst of star formation activity, though now the interstellar medium (ISM) is fur- ther enriched by the previous generation of stars. The efficiency of GC formation decreases in comparison with field stars, possibly due to the enriched ISM (Forbes et al., 1997). GCs formed at this time will have higher metallicities than the ones formed in the initial phase.

The exact nature of the protogalactic cloud (i.e density, clumpiness) determines the final abundances of blue and red GCs. Very dense and clumpy regions, for example, will produce a larger number of blue, metal-poor GCs (Forbes et al., 1997). While the model can reproduce the observed bimodality in GC systems, a large problem arises in defining the process that truncates the formation of blue globular clusters. It has been suggested that the formation of blue GCs was truncated at the epoch of reionization (Zaroubi, 2013) at z = 6 − 12. However, there are few simulations to date which test this model. A study by Beasley et al. (2002) used semi-analytic modelling and investigated the abundances and distribution of globular clusters which formed at z = 8-0. While bimodality arose in 93% of the simulated systems, the model results were strongly dependent on the exact value for the truncation epoch. Findings by Cote et al. (2003); Pota et al. (2013b); Richtler et al. (2014) support the scenario by Forbes et al. (1997) of multiphase dissipational collapse (see Section 1.4.3). 14 Chapter 2. Simulations

2.1.2 Globular clusters in cosmological simulations

A significant problem in large-scale GC simulations is the accurate recreation of observed properties, such as the presence of metallicity bimodality, of GC systems. In the example of bimodality, it is not only necessary to create a bi or multi-modality but the fraction of metal-poor (blue) GCs vs metal-rich (red) GCs also have to match observed values. Since there is no clear theory of GC system formation, simulations explore all possible kinds of scenarios. In the theory of two-phase galaxy formation (as proposed by Oser et al., 2010) the formation and growth of a galaxy is split into two distinct phases. In the first stages, star formation is supplied with material from cold gas inflows. The overall stellar metallicity would be metal-poor and any GCs that form would be classified today as blue. This early phase very closely resembles the model of monolithic collapse (Larsen, 1975). In the second phase, referred to as the ‘accretion phase’, stars and GCs are accreted in a series of major and minor mergers. These mergers are largely ‘dry’ mergers, since no new gas is brought in and will not trigger bursts of star formation. While no new stars are formed in the accretion phase, Oser et al. (2010) highlights that for several simulated systems, the galaxy luminosity and stellar mass doubled. While the metal-rich components (red GCs) are located in the galaxys’ central regions, the outer regions are dominated by metal-poor objects brought in by in-falling older, less massive galaxies. In NGC 1407 Forbes et al. (2011), found strong metallicity gradients in both the blue and the red subpopulation, supporting the two-phase model formation scenario. In Moore et al. (2006); Ricotti et al. (2016) a number of dark matter subhalos distributed throughout the galaxy are used to form the individual GCs that make up the GC system. An example of a simulation that form GCs in major mergers is Gnedin & Prieto (2009), who used n-body simulations from Kravtsov & Gnedin (2005) to explore the formation of both metal-rich and metal- poor globular clusters. In this model GCs formed in protogalactic discs during massive wet mergers of galaxies that peaked at 3 < z < 5 (Kravtsov & Gnedin, 2005). They assumed inner-halo clusters were originally accreted from smaller satellite galaxies that got disturbed during the merger and populated regions out to ∼60 kpc. Anything farther out than 60 kpc is defined as an outer halo cluster in the remaining satellite galaxies. Both blue and red GCs undergo the same formation process and only differ in where and 2.1. Simulations of globular cluster system formation and evolution 15 when they formed (Gnedin, 2010). Major mergers appear to contribute equally to the populations of red GCs and blue GCs, whereas early mergers of low mass progenitors appear to only contribute to blue GCs (Gnedin & Prieto, 2009). Early smaller mergers form a few blue GCs every time while the much greater number of GCs for each merger lead to an abundance of red GCs. The overall study is kept basic but it explains the formation of GC bi-modality. The simulation by Gnedin & Prieto (2009) has not yet been tested on elliptical galax- ies, however, there is an assumption that elliptical galaxies undergo a greater number of massive mergers forming a greater fraction of red GCs (Gnedin & Prieto, 2009). A similar study was perfomred by Muratov & Gnedin (2010). In their N-body simulations, they merged Milky Way sized protogalactic halos and studied the emerging GCs. The simulation was designed so that GCs did not form outside the major merger events and mergers would not take place for z < 1. They found the mass of globular clusters formed in mergers is dependent on the mass fraction of gas available. As described in Gnedin & Prieto (2007) globular cluster metallicity is linked to its host galaxy at the time of the merger. Muratov & Gnedin (2010) found that their model produced an excess of blue GCs, which were not disrupted in their lifetime. A more recent adaptation of this simulation by Li & Gnedin (2014b) tests whether GCs formed during major mergers or whether a galaxies specific star formation rate (sSFR) could be linked to GC formation (i.e once a certain level of sSFR is reached GC formation is triggered). The simulation itself aimed to recreate GC system formation in a cluster environment and allowed them to directly compare their results to Virgo cluster galaxies. Their initial halo masses ranged between 12 13 2 × 10 M and 7 × 10 M . Bimodality arose through different merger epochs as well as differences in host galaxy masses. Metal poor GCs (MPGCs) formed in early, less massive mergers with formation peaking around z∼ 4-6. Metal rich GCs (MRGCs) formed in the more massive, late time mergers over a more extended time period down to z=1. On average MRGCs were 5 Gyrs younger than MPGCs. The resultant metallicity distribution was comparable to those of Virgo cluster galaxies. They also used sSFR as a trigger for GC formation, which however did not yield metallicity distributions that matched observations. 16 Chapter 2. Simulations

A mixture of semi-analytic models and N-body simulations was used by Bekki et al. (2008) to investigate GC properties as part of galaxy formation. All GC formation in this scenario is intrinsically linked to the formation of galaxies via hierarchical merging. In cosmological simulations ∼ 12.8% of all galaxies were able to form GCs before the artificial truncation at z = 6. The simulation assumes that GCs form within the host galaxy’s main dark halo from giant molecular clouds (GMC) (Bekki, 2003), without any associated DM subhalos. While its host galaxy is undergoing a major merger, the external gaseous pressure of the GMC will increase, triggering gravitational collapse and a subsequent burst of central star formation. The centralized burst of star formation forms a compact self- gravitating cluster. In a previous study Bekki et al. (2002) presented the possibility for GCs to form outside virialised galaxy halos while still being associated with a . These intra-cluster GCs (Bekki et al., 2002) originally form through tidal stripping, and make up ∼ 20 − 40% of GCs in massive galaxy clusters. Katz & Ricotti (2014) used cosmological n-body simulations to trace mergers and interactions between a Milky Way (MW) sized object and several smaller satellites with 9 DM masses ∼ 10 M . The simulation covered a range of z = 104.3 to z = 0 and was subdivided into 27-time steps. Globular clusters were first assigned to a halo at the time of virialization. Their initial masses were chosen at random from a previously calculated globular cluster initial mass function (GCIMF) and ranged between 1.07×107 7 M and 2.9×10 M (where the occurrence of the very large objects is very unlikely). At each time step, the number and masses of globular clusters in each halo were calculated. A key point of their simulation was the study of three evolutionary tracks that globular clusters could follow:

• Globular clusters which formed in-situ in the MW sized halo or in a subhalo but did not undergo an accretion event.

• Globular clusters which were accreted by a larger galaxy but did not fall into the MW sized halo.

• Globular clusters which were eventually accreted onto the MW sized halo.

The simulation of Katz & Ricotti (2014) was run four times, each time utilizing a model that operated under slightly different assumptions, such as the value of globular cluster 2.1. Simulations of globular cluster system formation and evolution 17 formation efficiency used. In the end the model KR13-bis was the one that best reproduced observational properties such as GC positions, GC velocities, peak masses, metallicities, GC ages as well as galaxy specific frequency. The KR13-bis model assumes globular clusters from in a single large burst with metallicities consistent with the host galaxy at the time of formation. Therefore, high mass hosts would have formed MRGCs, whereas MPGCs would have formed in dwarf galaxies. A total of 146 globular clusters survived until z = 2. Out of these 146 globular clusters, 62% did undergo one or more accretion events (i.e they were accreated onto the galaxy). The peak globular cluster formation epoch were observed at z ∼ 2 and z ∼ 7 − 12. For a complete list of results see tables 1-3 in Katz & Ricotti (2014). A similar simulation was performed by Tonini (2013a), who also used initial halo 11 9 masses between 10 M and 10 M and traced globular clusters through merger events until z = 0. However the simulation did not include n-body calculations: instead the study used semi-analytic modelling and Markov Chain Monte Carlo (MCMC) techniques. Tonini (2013a) assumes no bimodality in globular cluster populations within DM halos 9 with masses less than 10 M . Tonini (2013a) created merger trees for the final set of selected galaxy halos based on theoretical merger rates as a function of mass and redshift from the Millennium simulation (Springel & Hernquist, 2003). Globular cluster metallicities were, as in Katz & Ricotti (2014), based on their parent galaxies’ metallicity at the time of formation. The simulation by Tonini (2013a) showed that a direct result of the hierarchical galaxy assembly scenario was a bimodal metallicity distribution in globular cluster systems, where MRGCs are generally formed in-situ to the main progenitor and MPGCs form in low mass satellites. Additionally, they found a peak in GCs formation at 6 z ∼ 2. However the globular clusters in Tonini (2013a) only have masses up to ∼ 10 M . As stated before, the model by Tonini (2013a) assumes that GC bimodality is absent in 9 galaxies with DM halo masses smaller than 10 M . The results from their simulation show that bimodality becomes less pronounced at higher redshifts and does completely disappear at z ∼ 2. A study specifically aimed at testing the multiphase collapse model was undertaken by Beasley et al. (2002). In Beasley et al. (2002) used merger trees within a semi analytic model and investigated the abundances and distribution of the surviving GCs. Initial halo 18 Chapter 2. Simulations

13 15 masses (Mh) of the merger tree were between 1.0 × 10 ≤ Mh ≤ 1.3 × 10 . Formation of MPGCs was manually truncated at z = 5; the truncation is somewhat arbitrary but necessary in order to not overproduce MPGCs. The giant gas clouds that formed globular clusters had a mean cold gas-fraction of 95%. They had a final 450 realizations of elliptical galaxy globular cluster systems. Mean ages for globular cluster were ∼ 12 Gyrs and ∼ 9 Gyrs for MPGCs and MRGCs. Bimodality arose in 93% of galaxies. The E-MOSAICS study by (Pfeffer et al., 2018) uses EAGLE simulation data to track the formation of GCs in galaxies using a combination of hydrodynamic and semi-analytic 6 modeling. In the simulation a GC is defined as star clusters exceeding the mass of 10 M and a radius of 3−10 pc measured from the cluster’s center. They investigated 10 Milky Way like galaxies and found that the environments that favor the formation of GCs are in the early universe and/or galaxy mergers. GCs are formed in situ or can be accreted during galaxy interaction events. Due to the higher gas pressure and surface density in the early universe, GCs were largely old objects (z>1). Cluster formation efficiencies reached their peak at z= 1 - 4. Additionally those galaxies which formed earliest or had particularly low metallicities were able to form star clusters much more efficiently. Pfeffer et al. (2018) further states that the the initial GCs formed in the simulation are reminiscent of young massive clusters we observe in the nearby universe. Further work is being done on both assessing the properties of these 10 simulated galaxies and their GC systems as well as in broadening the spectrum of input galaxies to include elliptical and lenticular galaxies. While there is a wealth of information to be gained from simulations, there are only a few predictions made (in terms of age, distribution, abundances etc.) that we could test observationally. One simulation that looks at the direct effect of galaxy formation on GC system properties is by Choksi et al. (2018), who analyzed the Illustris simulation. Therein, GC formation is not triggered by mergers but GCs are first formed in situ and then accreted. The Illustris simulation covered a redshift range of z = 47 - 0 where blue (metal poor) GCs originated from smaller halos at redshifts between z = 7 - 5 and red GCs formed in massive halos at z = 4 - 2. Half of their GCs formed at a redshift range of 11 12.5 5

3 Observations

3.1 Data Collection and Reduction

The imaging data of NGC 4526 was obtained in 2010 using the Suprime-Cam imager on the Subaru telescope as part of the SAGES Legacy Unifying Globulars and GalaxieS (SLUGGS) survey. Suprime-Cam consists of ten 2048 x 4096 CCD detectors arranged in a 5 x 2 pattern, with gaps between each CCD. Overall Suprime-Cam has a field of view of 34 x 27 arcmin and a pixel scale of 0.202 arcsec. Exposures were taken in the g, r and i bands with total exposure times of 1140s, 1310s and 610s respectively. The seeing in the g and i bands was 0.6 and 0.5 arcsec respectively, whereas the seeing in the r band ranged between 0.75 and 1.2 arcsec with its median at ∼1 arcsec. As there are gaps between the CCDs, multiple exposures were taken in a dithered pattern to image the full extent of the galaxy without leaving any blank regions. The dithered images were later aligned and combined to create a mosaic of NGC 4526. Data reduction was carried out by A. Villaume at UCSC using the pipeline SDFRED (Yagi et al., 2002; Ouchi et al., 2004), which includes scripts for flat fielding and atmospheric dispersion corrections. Also this pipeline bias subtracted, flat-fielded and combined data into a single mosaic.

21

4 Photometry

4.1 Galaxy light model

Before trying to identify globular cluster candidates belonging to NGC 4526 from the composite image, we modelled and subtracted the galaxy light. This will improve our detection of sources later on. We used the IRAF software package ISOFIT developed by B. Ciambur (see Ciambur, 2015) as it is able to produce more accurate models for non- elliptical, edge-on galaxies than the standard ELLIPSE task in IRAF which is generally used in comparable projects. ISOFIT is well-suited to our approach because it allows the galaxy light to be modelled with higher-order harmonic functions than those used in the standard task ELLIPSE, producing more accurate models for galaxies with non-standard shapes. We found that we could not get an accurate model for the inner region of NGC 4526 without including harmonics from 2-8 (higher level harmonics made available by ISOFIT) in the fit. We allow ellipticity, position angle and the center of the object to vary as free parameters in the fit.

There are two bright foreground stars within ∼ 7.5 arcmin of NGC 4526 whose scat- tered light extends to radii of ∼ 4 arcmin. As the galaxy and stellar light overlap signif- icantly, we first used ISOFIT to model and subtract the light from the two stars before applying it to the galaxy itself. We then created separate models for all three bands and subtracted them from the original images, producing residual images that were subse- quently used in object detection.

23 24 Chapter 4. Photometry

4.2 Object detection

We used the software Source Extractor (SExtractor) (Bertin & Arnouts, 1996) to identify objects and their properties from our residual images. As outlined in Annunziatella et al. (2013), SExtractor performs comparatively better at fainter magnitudes than DAOPHOT. Our input parameters were very similar to those used by Kartha et al. (2014), who per- formed a similar study using Subaru imaging of three elliptical galaxies. Here, we selected sources which had at least five adjacent pixels, and which were 5 σ above the local back- ground instead of 3 σ as used by Kartha et al. (2014). This allowed us to cut down on false source detection without effecting how many real sources we recovered in the com- pleteness tests. We based the optimum size of detection and background apertures on a curve of growth1 constructed for a number of globular cluster candidates. We determined the optimum aperture radius in g, r and i to be 0.95 arcsec, 1.1 arcsec and 0.95 arcsec for globular cluster candidates and 1.2 arcsec, 1.3 arcsec and 1.2 arcsec for Ultra Compact Dwarf (UCD) candidates (possibly the stripped cores of galaxies; see Brodie et al. (2011) for an in-depth discussion). Although the higher harmonics improved the model, we still had to exclude the very central regions of the galaxy during object detection (i.e. within the innermost 15 arcsec) due to this region being partially overexposed. When SExtractor is run it finds not only point sources such as globular cluster can- didates but also other objects such as galaxies and foreground stars. In order to remove spurious noise, we matched all objects in all bands within a positional tolerance of 0.2 arcsec. By the end of this process, we had extracted a total of 32100 objects.

4.3 Zeropoint estimates and final magnitudes

To determine the appropriate zeropoints in g, r and i we compared our instrumental mag- nitudes (measured using the apertures quoted in Section 4.2) of all the objects recovered by SExtractor to their equivalent as measured by NGVS (Ferrarese et al., 2012) using the Canada-France-Hawaii Telescope (CFHT). We matched both object lists in RA and Dec allowing for a maximum difference of 0.2 arcsec. The public data archive includes

1Measuring the aperture magnitude at increasing apertures and determining the aperture where any further increase in size will not increase the measured magnitude 4.4. Globular cluster candidate selection 25 full CHFT coverage of NGC 4526 in g and i; however, the CFHT r-band was incomplete and only covered about a quarter of the galaxy. Nevertheless we recovered ∼ 19000 ob- jects in g and i and ∼ 3600 objects in the matched g, r and i imaging. We calculated the median offset between the extinction-corrected CFHT and Subaru magnitudes in a range between 17.5 to 20 mag in g, 17 to 20 mag in r, and 18 to 20 mag in i. After applying the initial offset we refined the offset calculation by excluding any distinctive outliers with an offset larger than |∆mag|= 0.2. The final median offsets in g, r and i were translated into non-extinction-corrected 1-second zeropoints of 20.76 mag, 20.86 mag and 21.80 mag respectively using the exposure times stated in Section 3.1. Over the same range of magnitudes, the root mean square (rms) of the difference between the CFHT and Subaru magnitudes is g = 0.056 mag, r = 0.052 mag and i = 0.057 mag. We used the rms measurements in each band as the uncertainties in the zeropoints. The Galactic extinction values applied by NGVS (which we adopt) are 0.084 mag, 0.061 mag and 0.041 mag in g, r and i and were taken from the dust maps of Schlegel et al. (1998). All magnitudes quoted in this paper are extinction-corrected aperture magnitudes.

4.4 Globular cluster candidate selection

We applied a variety of selection criteria to identify globular cluster candidates from the complete list of objects found by SExtractor in all three bands. We first removed all objects for which it was not possible to measure magnitudes in all three bands as well as those on the very edges of the image where there was high noise and a prevalence of spurious objects. We therefore only considered objects within a distance of 12 arcmin from the galaxy’s centre, which is equivalent to 22 galaxy Re (based on the measurement of Forbes et al. (2017a)).

We initially only consider objects that occupy a magnitude range of 19 ≤ i ≤ 22.5 (Figure 4.1). The fainter magnitude limit was chosen to be the globular cluster turnover magnitude Mi= -8.5 mag (i=22.5 mag), which we derived using the GC turnover magni- tudes of the Jord´anet al. (2009) sample and converted using the colour transformations described by Usher et al. (2012). We converted apparent to absolute magnitude assuming the distance to NGC 4526 to be 16.4 Mpc (Brodie et al., 2014). The upper magnitude 26 Chapter 4. Photometry

Figure 4.1 Colour-magnitude diagram showing the selection of globular cluster candidates in NGC 4526 using the selection criteria as described in Section 4.4. The complete sample of objects extracted from Subaru images is shown in black; the globular cluster candidates that passed all our selection criteria are shown in green. GCs, as identified by Jord´an et al. (2009) from HST imaging, that lie inside our selection criteria are shown in red, while the objects that lie outside our selection are shown in yellow. The blue lines signify the selection criteria imposed on colour and magnitude (0.45 ≤ g -i ≤ 1.3 mag and 19 ≤ i ≤ 22.5).

cut of i= 19 mag (Mi = -12 mag), on the other hand, was chosen to separate globular cluster candidates from foreground stars as well as UCDs and was based on a study by Brodie et al. (2011). We next selected in colour-colour space, which was based on the NGVS globular cluster selection as described in Durrell et al. (2014) and is displayed in Figure 4.2. Our colour-colour range to select globular clusters is 0.45 ≤ g − i ≤ 1.3 and 0.25 ≤ g − r ≤ 0.75.

A selection in magnitude and colour-colour space was insufficient to remove objects such as background galaxies, as purely through a visual inspection we could still identify many background galaxies in the sample. We therefore applied a further selection cut 4.4. Globular cluster candidate selection 27

Figure 4.2 Colour-colour diagram showing the selection of globular cluster candidates in NGC 4526 using the selection criteria described in Section 4.4. The complete sample of objects extracted from Subaru images is shown in black; the globular cluster candidates after all our selection criteria had been applied are shown in green. GCs, as identified by Jord´anet al. (2009) that lie inside our selection criteria are shown in red, while the objects that lie outside our selection are shown in yellow. The blue lines signify the selection criteria imposed on both colours (0.45 ≤ g − i ≤ 1.3 and 0.25 ≤ g − r ≤ 0.75). 28 Chapter 4. Photometry using the SExtractor CLASS STAR (CS) output. The CS parameter is calculated by SExtractor using a neural network and describes how star-like an object is, incorporating information about its ellipticity and light profile. CS values range between 0 and 1, where 1 is most star like. To be classed as a possible GC candidate, we used a Pritchet function (Carney & Harris, 1998) to describe our CS limits as a function of i mag for the full object sample: 1 0.65(i − 24.2) CS ≥ (1 − ) − 0.05 (4.1) 2 (1 + 0.652(i − 24.2)2)0.5 where i is the object’s in the i band. We include the offset of 0.05 as otherwise this selection criterion would become too stringent for bright (i≤20 mag) sources. The full sample of selected GC candidates and their CS values in the i band are shown in Figure 4.3. We only applied the CS selection criteria in the i band as it was our deepest band and it provided the most accurate measurements of the CS parameter. After this final stage of GC candidate selection, we had 343 objects remaining. A spatial distribution of our candidate list can be found in Figure 4.4, where objects are colour-coded by their associated subpopulation (see Section 4.5 for details). The figure also shows the region (shaded red) and objects used to determine the level of background contamination between 10.8 arcmin and 15.8 arcmin.

Objects that pass our selection criteria but are brighter than our upper magnitude cut are suspected to be ultra compact dwarfs (UCDs). We constructed a separate catalogue of potential UCD candidates using the same selection criteria as for GCs but changing the brighter and fainter magnitude cut. The brighter level magnitude cut is simply i=19 mag (i.e the upper GC magnitude cut), however it was more difficult to define a distinct upper magnitude limit for UCDs. This difficulty was described in the paper by Brodie et al. (2011), who did not apply an upper magnitude limit in their study. Here we adopted an upper limit for UCDs, which was set at i = 17 mag (Mi= –14 mag). For NGC 4526 we found 36 UCD candidates. All UCD candidates underwent and passed a visual inspection of their size and shape (i.e large and mostly circular in appearance). 4.4. Globular cluster candidate selection 29

Figure 4.3 The selection of globular cluster candidates in NGC 4526 using the CLASS STAR (CS) parameter. We only selected objects above the blue line which represents the respective minimum CS values at a given magnitude.The equation for this line is given in Equation 4.1. The blue dashed lines represent the cuts in magnitude as described in Section 4.4 and displayed in Figure 4.1. The complete sample of objects extracted from the Subaru images is shown in black; the globular cluster candidates are shown in green. Globular clusters, as identified by Jord´anet al. (2009) from HST imaging, that lie inside our selection criteria are shown in red, while the objects that lie outside our selection are shown in yellow. 30 Chapter 4. Photometry akrudcontamination. background lseshv a have clusters Figure . h itiuino lblrcutrcniae oorcddb hi upplto seScin45 leglobular Blue 4.5. Section (see subpopulation their by coded colour candidates cluster globular of distribution The 4.4 g − i oorls hn09 a.Tesae e ue icedfie h ue nusw s odfietelvlof level the define to use we annuls outer the defines circle outer red shaded The mag. 0.96 than less colour 4.5. Globular cluster colour distribution 31

4.5 Globular cluster colour distribution

To determine whether the GC system in NGC 4526 is bimodal in colour, we first had to define and then remove any background contamination. We defined an annulus around the area of globular cluster detections (shaded red area in Figure 4.4). The background annulus begins at 10.8 arcmin (20.1 Re) from the galactic center and stretches out to

15.8 arcmin (29.3 Re). These radii were chosen from the galaxies’ globular cluster surface density profile(see Section 5.2). The full outer annulus encompasses approximately half the amount of area compared to the area of detection. We detected 84 objects in the outer annulus that passed our general GC selection criteria. Further inspection revealed their mean colour as g − i = 0.76 mag. We removed background contaminants by splitting both the GC candidate list and the background contamination catalogue (i.e. the objects in the outer annulus) into several colour bins. This lets us estimate how many real GCs we should have found in each bin. As we knew how many background objects we were expecting to find, based on the background annulus, we then created samples for each bin randomly drawn from our original non-background subtracted catalogue. This process was repeated 100 times in a bootstrapping manner. We further vary the size of the background annulus for each iteration to account for any uncertainties derived by the definition of our annulus size. Each background subtracted sample contains a minimum of 80 GC candidates.

To test for the bimodality of the GC system we utilized the Gaussian Mixture Mod- elling (GMM) code by Muratov & Gnedin (2010), which models distributions of objects as a number of Gaussian-distributed populations. This allowed us to determine the best fit to our data as well as ruling out the possibility of having a unimodal or possibly a trimodal system. Additionally, the GMM code returns the probability of each source be- ing associated with either subpopulation. The GC system of NGC 4526 presented itself as purely bimodal. GMM tests were conducted on the 100 background subtracted GC samples allowing for a different variance in each subpopulation. As shown in Figure 4.5, the bimodal model shows peaks, for its blue and red subpopulations, at g −i = 0.78  0.01 mag and 1.03  0.01 mag with respective values for the distributions’ standard deviations (σ) of 0.11  0.01 and 0.05  0.01. From our background subtracted sample of GCs, 73% 3% were associated with the blue subpopulation and 27% 3% were associated with the 32 Chapter 4. Photometry

Figure 4.5 A colour histogram of GC candidates after removing background contamination as described in the text. The blue and red lines are the GMM fits of the blue and red subpopulations respectively. We observe a bimodal distribution, with peaks of the blue and red subpopulations at g − i = 0.78  0.01 mag and 1.03  0.01 mag respectively. red subpopulation.

4.6 HST sources

ACSVCS carried out two-filter imaging for over 100 early-type galaxies in the Virgo Clus- ter with the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope (HST). Jord´anet al. (2009) investigated the globular cluster systems of these galaxies, and pub- lished their positions as well as their g and z magnitudes. For NGC 4526, Jord´anet al. (2009) found 244 globular clusters with half-light radii smaller than 10 pc. Of these 244 objects, 128 meet our GC selection criteria, this is largely because we excluded all objects fainter than 22.5 mag in i. To test our recovery process with SExtractor we matched our GC candidate list (described in Section 4.4) with the ACSVCS globular cluster list). We 4.7. Completeness 33

Table 4.1 The results of the GMM testing on the background removed candidate sample, where µ is the g − i value at which each subpopulation has its peak and σ is the standard deviation of the distribution. The value in n is the number of globular clusters associated with each subpopulation. Blue Red µ 0.78  0.01 1.03  0.01 σ 0.11  0.01 0.05  0.01 n 187  13 58  13 performed a positional match, accounting for a systematic offset in both RA and Dec (∼ 0.2” and ∼ 0.53” respectively). We were able to match 112 out of the 128 GCs in the ACSVCS GC list. The 16 sources that we did not recover fell within the innermost 25 arcsec from the center of NGC 4526. For said 16 objects we used the colour conversion described in Usher et al. (2012) to calculate their r and i-band magnitudes (g-band mag- nitudes were taken directly from Jord´anet al. (2009)). We believe this to be reliable as comparing colour converted magnitudes of the 112 recovered objects for which we also had our own measurements were consistent. All 128 objects that fell within our magnitude cut of i ≤22.5 mag were then included in our final catalogue. A full list of all GC candidates can be found in Appendix A.

4.7 Completeness

We estimated the level of completeness with respect to magnitude and galactocentric radius. To do so we first created an appropriate point spread function (psf) for each individual band using the makepsf function in IRAF. We used these psfs as an input for the DAOPHOT/addstar task to add 500 fake objects into each image. The fake sources occupied a range of magnitudes between 15 mag and 27 mag and were evenly distributed throughout the image. This process was repeated 50 times, leading to a total of 25000 objects added per band. Then we subtracted the galaxy light models described in Section 4.1 from all the newly created images and ran them through SExtractor to recover the added objects. Figure 4.6 (left panel) shows the completeness fraction (number of recovered objects over number of objects originally added to images) plotted against magnitude in each band. We achieved a completeness fraction of 50% at magnitudes of ∼25 mag, ∼ 24.3 34 Chapter 4. Photometry

Figure 4.6 Results of the completeness tests where the colours blue, green and red cor- respond to the g, r and i band respectively. Left: Colored points present completeness fraction in g, r and i versus magnitude. Due to the large numbers of sources that make up each data point the errors on the completeness fraction are smaller than the point size. The coloured lines are the Pritchett functions which best described the results in each band. We observe high levels of completeness up to magnitudes of ∼ 25 mag, ∼ 24.3 mag, ∼ 24.5 mag in g, r and i, where the completeness fraction drops below 50%. Right: A plot of completeness fraction with galactocentric radius. This plot was created only using sources within a magnitude range of i = 16 mag - 21.5 mag. Choosing this particular magnitude range ensures for 100% completeness in magnitude. The coloured lines are the Pritchett lines which best fit our results. 4.7. Completeness 35 mag, ∼ 24.5 mag in g, r and i. The second panel (right) of Figure 4.6 shows completeness in terms of galactocentric radius using only objects between i = 16 - 21.5 mag, where we observe 100% magnitude completeness. The drop in completeness close to the galactic center is due to the brightness of the galaxy, where we were unable to remove all the central galaxy light completely.

5 Analysis

5.1 Radial colour gradients

We created background-subtracted samples to ascertain the presence of a colour gradient in either of the two subpopulations. The process was similar to the one described in Section 4.5 with the added difference that we initially divided our sample of GC candidates into radial bins (no fewer than 60 GCs per bin before background subtraction) and then scaled the number of background objects according to the size of each bin. This process was then repeated 50 times before calculating the mean colour for either subpopulation at every radial interval. As the number of GCs associated with the red subpopulation was low (94 objects before background subtraction) we did not have enough radial bins to determine a possible trend in the red subpopulation. Figure 5.1 shows the binned mean colour with galactocentric radius in the blue and red subpopulation, as well as the fit to the blue subpopulation data points. The error bars are Poisson errors and there is a minimum of 33 GCs in each blue bin and a minimum of 18 GCs in each red bin. The further the centre of a bin is from the centre of NGC 4526, the fewer objects remain after background subtraction. Our line of best fit for the blue subpopulations was:

(g − i)blue = −0.096 (0.1) log R + 0.81 (0.07) (5.1) where R is the galactocentric radius. Comparing the slope of the line with the error on

37 38 Chapter 5. Analysis

Figure 5.1 Mean colour versus galactocentric radius for the GC candidates in NGC 4526. Blue and red points are the mean colour of each subpopulation (after subtracting back- ground contamination). We have insufficient data for the red subpopulation to determine a colour gradient.There was no significant colour gradient observed in the blue. The dashed line represents the best fit for the blue subpopulation. the slope we came to the conclusion that the trend is not significant.

5.2 Surface density profiles

We investigated the variation of the surface density of globular clusters with respect to galactocentric radius for the entire globular cluster system as well as for the individual subpopulations. We divided GCs into radial bins with a minimum of ∼ 25 GCs in each bin and used these bins to calculate the surface density. As our inner region is supplemented by ACSVCS data, we assume our GC candidate list to be complete up to the turnover magnitude (see completeness graphs in Section 4.7). We therefore did not apply any completeness corrections. In Figure 5.2 we fit the Sersic profile as given in Graham & Driver (2005). In the fitting process we divided our sample into ACSVCS objects and those 5.2. Surface density profiles 39 objects recovered from Subaru imaging, because our Subaru data set needed to undergo background subtraction while the ACSVCS sample did not. We subtracted backgrounds using the method described in section 4.5 (see Table 5.1 for background level values) from our Subaru sample before fitting the Sersic profile to the complete sample of both the background subtracted Subaru candidate list and the ACSVCS GCs. As the background subtraction was a statistical process, we created 200 background-subtracted samples to be independently used in the Sersic fits. From the resultant fits we estimated our errors as well as the overall best fit for our background subtracted data. The Sersic index and the effective radius of the GC system (using the combined ACSVCS and Subaru data) were n = 1.99  0.05 and Reff = 1.68  0.07 arcmin. When fitting a power law to our total dataset we calculated a slope of -1.850.01, between galactocentric radii of 0.8 arcmin and 5.8 arcmin. We then separated our complete candidate list into subsamples containing only the blue or red subpopulation using a colour split at g − i=0.96 mag. At this colour a GCs probability to be associated with either the blue or red subpopulation was 50% each. The complete results of the Sersic fits for all samples can be found in Table 5.1. The red subpopulation was found to have a lower surface density compared to the blue subpopulation throughout most of the galaxy. The red subpopulation reaches background levels at ∼ 7.5 arcmin (36 kpc) whereas the blue subpopulation did not reach background levels until 10.8 arcmin (518kpc). In order to calculate the total number of GCs contained within the system associated with NGC 4526 we integrated our best fit Sersic profile and, because we originally set a cut at the turnover magnitude (i.e i ≤ 22.5 mag), we doubled the results. We proceeded in a similar fashion to calculate the number in the red and blue subpopulations. For the total GC system we found 375  50 GCs and 280  34 and 71  9 for the blue and red subpopulations respectively. As previously mentioned, this is not the first study examining the GC system of NGC 4526. Peng et al. (2006, 2008) previously conducted their own analysis based on ACSVCS data. Peng et al. (2006) focused on separating observed GC candidates into subpopula- tions, while Peng et al. (2008) focused on system properties such as the specific frequency and estimating the total number of GCs associated with the galaxy.In our study the GC system of NGC 4526 presented itself as bimodal with peaks at g − i= 0.790.01 and 1.02 40 Chapter 5. Analysis

Figure 5.2 Surface density profiles for the blue and red subpopulations as well as the complete GC system of NGC 4526. The surface density profiles were constructed using the GC candidates found in the Subaru and (had they not already been included in our Subaru candidate catalogue) radial velocity data, as well as GCs found by Jord´an et al. (2009) (i.e ACSVCS data supplementing the central regions), which have i band magnitudes brighter than the turnover (i.e i ≤ 22.5 mag). The orange line represents the best fit Sersic line to this data. Green points are derived using objects from ACSVCS data and orange points used our Subaru imaging after subtracting the background level of contamination as described in Section 5.2. Similarly, while the green and orange points and orange line represent our full sample of globular clusters, red and blue lines represent the red and blue subpopulations respectively. We observe a higher surface density of blue GCs throughout all of NGC 4526. The red shaded region highlights the region of the outer annulus we used to define our level of background contamination. The purple dotted line at 7.5 arcmin highlights where we approximate the background for the red subpopulation to begin. 5.2. Surface density profiles 41

Table 5.1 The results of the Sersic profile fitting to the surface densities of the total GC candidate sample and both the blue and red subpopulations. Where n is the Sersic index, Re is the effective radius of the globular cluster system, Ne is the surface density of GCs at the effective radius in arcmin−2 and bg is the background surface density in arcmin−2. The background is not a product of our fit but was previously subtracted from the complete sample of Subaru candidates. Total Blue Red n 1.99  0.05 1.68  0.06 1.31  0.07 Re [arcmin] 2.06  0.07 2.17  0.06 1.03  0.02 −2 Ne [arcmin ] 2.72  0.13 1.96  0.09 2.51  0.15 bg [arcmin−2] 0.44  0.04 0.34  0.07 0.08  0.03

 0.01 which is in agreement with the values in Peng et al. (2008). Peng et al. (2008) values were originally published in g − z, so for the sake of comparison we converted them to g − i using the colour conversion published by Usher et al. (2012). The total number of GCs estimated for NGC 4526 by Jord´anet al. (2009) is 388  117, which agrees with the results of our surface density analysis: 375  50 GCs in total. Our estimated fraction of blue globular clusters (80% 10) is higher than the result from Peng et al. (2008) (62%). Their measurement was corrected for missing area (due to their small field of view), how- ever as the blue to red ratio is increasing with galactocentric radius we believe for the extrapolation to need adjusting to reflect the different subpopulations more accurately.

6 Global relations of GC systems

6.1 Radial extent of GC system

The effective radius of NGC4526’s GC system, as well as the respective subpopulations, were estimated by fitting their Sersic profiles as described in Graham & Driver (2005). For the complete GC system, containing both the blue and red subpopulations, we measured the effective radius to be at 2.06 arcmin (∼ 9.9 kpc). When we investigated the blue and red subpopulation separately, the effective radii were at 2.17 arcmin (10.4 kpc) and 1.03 arcmin (4.9 kpc) respectively. We define the total extent of the GC system as the point at which we have zero GC counts above the background level. The fitted Sersic profile also allowed us to determine to total extent of the GC system, which for NGC 4526 is 56 kpc (11.6 arcmin).

The extent of a GC system has been related to its host galaxy stellar mass. Studies that have related GC system extent and galaxy stellar mass include (among others) Rhode et al. (2007),Rhode et al. (2010) and Kartha et al. (2014). The host galaxy mass is estimated using mass to light ratios as described in Zepf & Ashman (1993) and the galaxy’s absolute visual magnitude. The mass is highly dependent on the mass to light ratio chosen. For red galaxies with little to no ongoing star-formation, Forbes et al. (2017a) used a value of log M/Lg = 0.7. In turn this value had been adopted from the study of Bell et al. (2003), who calculated present-day stellar mass-to-light ratios by fitting optical/NIR galaxy observations from SDSS and 2MASS. We used the same value in all

43 44 Chapter 6. Global relations of GC systems our calculations. In Figure 6.1 we use the dataset described in Kartha et al. (2014) to relate the properties of the GC system of NGC 4526 to other lenticular and elliptical type galaxies (26 lenticular and elliptical galaxies). We observed a relationship in GC system extent with host galaxy stellar mass (lower left panel in Figure 6.1) and plot a best fit line with a slope of 0.74  0.42. The higher the host galaxies stellar mass the greater the extent of the associated GC system. However, due to the associated error we deem the slope not significant. There appears to be no relation between a galaxy’s environmental density1 and the extent and the ratio of blue to red GCs (see Figure 6.1). We further compared the results for NGC 4526 to early-type galaxies in the sample of Forbes (2017). In Figure 6.2 we took two key figures from their original paper, which show the ratio of the size of a globular cluster system to its host galaxy size vs. galaxy size and effective radius of the globular cluster system vs. host galaxy stellar mass, and added NGC4526 (shown in orange) into each of them. While there was no trend for the ratio of the size of a globular cluster system to its host galaxy with galaxy size there was a trend in the second plot indicating that with increasing galaxy mass the effective radius of GC systems increases. NGC 4526 and its GC system did fall onto the general trend compared to the other galaxies and did not present itself as an outlier in any way. For further information on the galaxies and relations described in Figure 6.2 see Forbes (2017) for details.

6.2 Mass and dark matter fraction

One of the most fundamental properties of a galaxy is its mass. Measuring the total mass (i.e stellar and dark matter mass combined) of a galaxy is not a trivial task. We can not directly observe a galaxy’s dark matter but we can observe the gravitational effects caused by the presence of dark matter. Therefore we can assume that the distribution and kinematics of tracer objects (such as planetary nebulae or GCs) are directly linked to galaxy total mass distribution (Watkins et al., 2010; Alabi et al., 2017). Alabi et al.

(2017, 2016) used the radial velocity (RV) measurements of GCs within 5 Re to calculate the total (stellar + dark matter) mass of galaxies by using them as tracer mass estimators.

1We adopt the definition as used by Kartha et al. (2016) such that the environmental density is defined as the number of galaxies per Mpc3 and is taken from the Tully (1988) catalogue. 6.2. Mass and dark matter fraction 45

Figure 6.1 Comparison between properties of the GC system of NGC 4526 (orange) and other lenticular (purple) and elliptical type galaxies (blue stars) taken from Kartha et al. (2014). Upper left: The ratio of blue to red GCs (Nb/Nr) compared to their host galaxies’ stellar mass. NGC 4526 has one of the highest measured ratio of blue to red GCs at 3.89. There is no clear trend with galaxy stellar mass. Upper right: The ratio of blue to red GCs (Nb/Nr) compared to the density of their host galaxies’ environment. While the ratio of blue to red GCs in NGC 4526 is quite high (3.89) the galaxy itself does not fall into a high or low-density environment. We adopt the definition as used by Kartha et al. (2016) such that the environmental density is defined as the number of galaxies per Mpc3 and is taken from the Tully (1988) catalogue. Lower left: GC system extent with host galaxy stellar mass. The light blue line is the best fit to the data with a slope of 0.74  0.42. We include all galaxies when calculating this fit. NGC 4526 lies above this relation but within the scatter. Lower right: The GC system extent with the environmental density of the host galaxies’ position. NGC 4526 and its globular cluster system display average GC system extent and environmental density. NGC 4526 displays GC system extent and an environmental density in line with the rest of the galaxy sample. 46 Chapter 6. Global relations of GC systems

Figure 6.2 The ratio of the size of a globular cluster system to its host galaxy size vs. galaxy size (left panel) and effective radius of the globular cluster system vs. host galaxy stellar mass (right panel) taken from Forbes et al. (2017b). NGC 4526 has been added in orange. Red points represent data originally from Hudson & Robison (2017) and blue points data from the existing literature (see Forbes et al. (2017b) for details). The light blue line in the right panel is the best fit to the early-type galaxy data (excluding galaxies marked in red): log GC Re = 0.97 (0.4) log M? - 9.76 (4.4) as calculated by Forbes (2017). NGC 4526 is consistent with the general trend. 6.2. Mass and dark matter fraction 47

Simplified this means that studies such as Watkins et al. (2010) and Alabi et al. (2016) related the tracer number density to the galaxy’s total mass density through a power-law gravitational potential. We will be using the exact code that was used in Alabi et al. (2017). For the full mathematical derivations and processes please refer to Watkins et al. (2010, 2013); Alabi et al. (2016) and Alabi et al. (2017). The code outputs the total mass of the target galaxy and we can then infer the DM mass knowing the galaxy’s stellar mass. The stellar mass of NGC 4526 was estimated by Forbes et al. (2017b), it was then possible to for Alabi et al. (2017) to infer the dark matter mass and dark matter fraction

(fDM ) of the galaxy. Generally in order to estimate the mass of a galaxy the total GC system would be used. However, Forbes et al. (2016) explored the difference between using only a distinct subpopulation and using the whole system. Their results indicated that using the blue (i.e metal poor) subpopulation of a GC system would be a better indicator to study a galaxy’s dark matter content than using the red subpopulation. Additionally, they found a close relationship between the fraction of blue GCs and the host galaxy’s dark matter fraction. Out of the 16 galaxies they studied, they only found two outliers from this relation: NGC 4494, which has been shown to contain a tri-modal GC system (Foster et al., 2011) and NGC 4526 which is the main focus of this thesis. Spectra of GC candidates in NGC 4526 were obtained using the DEIMOS spectrograph on the Keck telescope. For NGC 4526 we have 107 measurements of GC candidate radial velocities Forbes et al. (2017b). We have measured aperture magnitudes for 106 of these objects, of which 90 meet our GC selection criteria. For the remaining GC candidate, because of its close position to the galaxy’s center we could not aquire a reliable measure of magnitudes and colour. Even though not all objects made the GC selection criteria cut they still are spectroscopically confirmed as GCs. We therefore conduct the dark matter fraction estimates using both the full sample of 106 GCs and the 90 GCs which meet our selection criteria. Respectively, each of these samples will be split into subsamples and labelled on whether we remove substructure and/or exclude low radial velocity sources. All objects with measured line-of-sight velocities <400 kms−1 are defined as so-called ‘low radial velocity sources’. Two more groups low radial velocity sources are defined at line-of-sight velocities <300 kms−1 and <250 kms−1 (see Figure 6.3).

A major difference between this comparison of the fraction of blue GCs (fb) and fDM 48 Chapter 6. Global relations of GC systems

Figure 6.3 GC candidates (106) with associated radial velocities vs. galactocentric radius. Purple hexagons are those GCs with measured radial velocities that are also contained in the ACSVCS catalogue. Orange circles represent GCs that have measured radial velocities and were matched with an object from our Subaru candidate list. The blue horizontal line that refers to the galaxy’s systemic velocity is 617 km/s (Cappellari et al., 2011). Red horizontal lines represent cuts to exclude low radial velocity sources (400 km/s, 300 km/s and 250 km/s) in the calculation of the dark matter fractions given in Table 6.1. Similarly the red shaded region highlights the GC radial velocities excluded in our final subsample. 6.3. Specific frequency 49 and the one undertaken in Forbes et al. (2016) is that we are using the updated effective radius of NGC 4526 as described in Forbes et al. (2017b), where Spitzer data was used to calculate Re. The Re measurement changed from 45 arcsec to 34.2 arcsec when using

Spitzer data. Finally we also calculated the total mass within 5 Re, 8 Re and Rmax subsamples. For the full sample of Rv sources we calculated fDM to be 0.88  0.02 at

Rmax. A full table of all samples and their fDM values at 5 Re, 8 Re and Rmax is given in Table 6.1. Rmax refers to the maximum radius to which we can measure fDM and is dependet on the number of GCs with RV measurements and their position. Each analysis assumes isotropy.

Using updated fDM values published in Alabi et al. (2017), who also used Spitzer data, we are able to plot the fb and fDM scaling relation as shown in Figure 6.4. The original scaling relation as calculated by Forbes et al. (2016) is shown in black. Using the effective radius, inferred from Spitzer data, we recalculated the best fit line which is shown in red. In the new fit we observe that the scatter in the relation has increased and we measured the new 1-sigma scatter to be 0.24 (red dotted line). As we ran multiple samples of radial velocity sources to estimate dark matter fractions we display the range of all possible values as a grey shaded box in Figure 6.4.

6.3 Specific frequency

The measurement of specific frequency is the measure of the overall number of GCs per unit starlight of its host galaxy.

0.4(MV +15) SN = N10 (6.1)

where N is the total number of GCs contained in the system and MV the absolute V- band magnitude of the galaxy (Harris & van den Bergh, 1981). The highest values of

SN are observed in dwarf elliptical galaxies (SN ∼ 15) and cD galaxies, while the lowest values are observed in spiral galaxies at SN ∼ 1 (Kavelaars, 1998). While we observe this characteristic in regard to galaxy types, the main variation is with galaxy mass (Miller & Lotz, 2007; Georgiev et al., 2010). Additionally, galaxies with a high specific frequency also appear to contain the highest proportion of blue (metal-poor) GCs (Forbes et al., 50 Chapter 6. Global relations of GC systems

Figure 6.4 Dark matter fraction scaling relation for 16 SLUGGS galaxies coloured by their stellar mass (Forbes et al., 2017a). The x and y axis are the dark matter fraction fDM and the fraction of blue GCs fb respectively. Where the light green outlying point is NGC 4494, an elliptical galaxy which in Foster et al. (2011) was described as having undergone a recent merger. NGC 4526 can be found to the left of the scaling relation at fb = 0.80 and fDM = 0.83. The sample of GCs used to calculate this point excluded those sources that had both low radial velocities(≤ 300) and exceeded a distance from the galaxy’s center of 1.5 arcmin (red shaded region in Figure 6.3). The sample was further corrected for substructure. The grey shaded region represents the possible placement for NGC 4526 given the results from Table 6.1. Black and red lines represent the original and the new best fit to the data (rms-scatter shown as dotted lines). We observe that compared to the results published in Forbes et al. (2016) the scatter in this relation has increased from 0.16 to 0.22. 6.3. Specific frequency 51

Table 6.1 Dark matter fraction values out to 5, 8 Re and Rmax using various sub-samples of measured GC radial velocities. Samples include those that exclude substructure and/or low radial velocity sources. If a sample excluded certain GCs the selection criteria are added to the sample name in brackets. Also two samples are included with the description ‘high radius, low RV’, in which we excluded all RV sources that had a velocity dispersion ≤ 300 kms−1 and were positioned more then 1.5 arcmin from the galaxy center.

substructure 5 Re 8 Re Rmax fDMRmax removed fDM no 0.54  0.10 0.67  0.06 20.32 0.88  0.02 fDM (RVs ≤ 400 km/s) no 0.15  0.2 0.46  0.11 16.75 0.73  0.05 fDM (RVs ≤ 300 km/s) no 0.34  0.15 0.54  0.09 16.75 0.76  0.04 fDM (RVs ≤ 250 km/s) no 0.48  0.11 0.63  0.07 20.32 0.87  0.02

fDM yes 0.47  0.12 0.63  0.07 20.32 0.87  0.02 fDM ( RVs ≤ 400km/s) yes 0.13  0.20 0.44  0.11 16.75 0.72  0.04 fDM ( RVs ≤ 300km/s) yes 0.34  0.15 0.55  0.08 16.75 0.77  0.04 fDM (RVs ≤ 250 km/s) yes 0.49  0.12 0.65  0.07 20.32 0.86  0.03 fDM (high radius, low RV) no 0.48  0.12 0.63  0.07 16.75 0.80  0.03 fDM (high radius, low RV) yes 0.53  0.10 0.67  0.06 16.75 0.83  0.03

1997; Brodie & Strader, 2006). A galaxy’s environment has been shown to influence its specific frequency, such that when comparing elliptical galaxies in both cluster and field environments, those inhabiting clusters have higher specific frequencies than their field counterparts (Harris et al., 2013). The observed trend with environment may however be a product of the underlying trend with mass as was suggested in Spitler et al. (2008). We calculated the total number of GCs associated with NGC 4526 to be 375  50. The V band magnitude of NGC 4526 was taken from Peng et al. (2008), who quoted an absolute V band magnitude of -21.38 mag. From those measurements we calculated a specific frequency of 1.05  0.14. Previously ACSVCS calculated the specific frequency of NGC 4526 as 1.09  0.33 (Peng et al., 2008), which is fully consistent within the estimated errors. Other S0-type galaxies in the SLUGGS sample (NGC 1023, NGC 3115, NGC 3607, NGC 5866 and NGC 7457) have specific frequencies ranging from 1.3 to 2.9 with the mean value of 2 (Kundu & Whitmore, 2001; Cantiello et al., 2007; Harris & Harris, 2011; Jennings et al., 2014; Kartha et al., 2014). Of the sample of galaxies listed above, the field galaxy NGC 7457 has the highest specific frequency at 2.9 (Harris et al., 2013) and the lowest mass at log M? = 10.13 M (Forbes et al., 2017a). Out of all analyzed SLUGGS 52 Chapter 6. Global relations of GC systems lenticular galaxies, NGC 4526 has the lowest specific frequency, but its properties such as galaxy mass, environmental density, size and GC system size were not in any way unusual. 7 Discussion

One motivation for closer examination of the GC system of NGC 4526 was that it did not fall on the dark matter scaling relation described in Forbes et al. (2016). The relationship aimed to explore the connection between blue globular clusters and the dark matter content of a galaxy. There were several possible reasons why the literature data for NGC 4526 made it appear as an outlier. One was that the limited field of view of the ACS camera images meand extrapolation was required in order to predict what the full extent of the

GC system would look like. Moreover the original fDM values were only preliminary and were later subject to a number of corrections (such as calculating pressure and rotation supported mass separately and combining them for the total mass, see Alabi et al., 2016). Finally there was the possibility that NGC4526 was indeed an unusual system and only the study of its subpopulations would indicate exactly why and how it got to be such an unusual case. Having analyzed the GC system of NGC 4526, we now have to connect our findings to the various formation scenarios we discussed in Section 1. For this purpose we review the observable differences for the formation scenarios and simulations:

• Major mergers: In the major merger model as described by (Ashman & Zepf, 1992) the earlier forming blue GCs will be redistributed by later mergers which form the red subpopulation. For that reason the red subpopulation would be more centrally concentrated. Ashman & Zepf (1992) predict that the blue subpopulation would exhibit stronger rotation compared to the red subpopulation.

53 54 Chapter 7. Discussion

• Hierarchical mergers: In the hierarchical merger model as described by (Tonini, 2013a) blue globular clusters are ∼ 1.5-2 Gyr older then the red subpopulation as they form earlier and then accrete in late mergers. In turn the red subpopulation appear more centrally concentrated. Depending on the exact nature of the galaxy’s merger tree (i.e fewer or more major to minor mergers) the ratio of blue to red GCs would be affected.

• Multiphase collapse: As described in Forbes et al. (1997) the blue subpopulation forms before the red (and the main bulk of the galaxy’s stellar content) making it considerably older. The late-forming red subpopulation is more centrally concen- trated and exhibit kinematics similar to the host galaxy stars. Forbes et al. (1997) also comments that the GC formation efficiency in the second collapse is decreased. We would therefore expect a blue GC fraction of 0.5 or above.

• Two-phase: In the two phase model as described by (Oser et al., 2010) we ex- pect to observe the kinematics of the red subpopulation to follow that of the host galaxy’s stars. Additionally the red subpopulation is predicted to be more centrally concentrated than the blue subpopulation.

From the simulations discussed in Section 2.1, the one we found to best describe NGC 4526 was Choksi et al. (2018), which explored the effect of major and minor mergers on the colour distributions of GC systems. Choksi et al. (2018) was build using the Illustris simulation. Also one of their initial assumptions was the GCs formed in-situ to galaxies and that the current structure within a GC system occurs due to ac creation. Exactly how their predictions compare to our own results will be discussed throughout the rest of this chapter. From the above described predictions, it quickly becomes clear that connection between the properties of GC systems and the formation of their host galaxies is not necessarily straightforward. Nevertheless they still provide us with a basis for conducting tests and identifying whether or not a galaxy has undergone recent interactions such as NGC 4365 (Blom et al., 2012) (a trimodal system) or NGC 4494, which exhibits an unusually low dark matter content (Foster et al., 2011; Forbes et al., 2017b). Dark matter offers an additional avenue for exploring possible unusual features in NGC 4526. 55

Before we dissect the various features of the GC system of NGC 4526, we note again that in terms of total numbers and mean colour for each subpopulation our results are in line with the previous studies by Peng et al. (2006, 2008). A feature that is immediately apparent in Figure 6.1 is the unusually high number of blue vs red GCs. We speculate that this is due to NGC 4526 having formed from a series of minor mergers (or largely dry mergers), which would increase the number of blue GCs without bringing in addi- tional red GCs or triggering a burst of star (and GC) formation. A formation scenario that closely matched this was described in the simulations by Choksi et al. (2018). They predicted a larger spread of colour in the blue subpopulation than the red. This occurs because the metallicity of blue GCs increases with host galaxy mass. Combining this with the assumption of a massive galaxy having undergone a large number of mergers, we can conclude that theoretically we should observe a higher spread of metallicities in its blue subpopulation. We can confirm that for NGC 4526 the calculated σ value is twice as high for blue GCs than for red GCs (see Table 4.1). During a series of minor mergers, the more massive an in-falling galaxy is the further towards the center of the host galaxy it will deposit its GCs. As there is a link between host galaxy mass and the metallicity of its GC system (Forbes et al., 1997; Blakeslee et al., 2010; Tonini, 2013b), the more centrally deposited GCs will have a higher metallicity than those in the very outskirts. This is because the more massive the galaxy that is merged in is the deeper into the existing galaxy it can penetrate before being fully assimilated. Taking this even further, with the statement that we can translate a colour into metallicity (or vice versa) as demonstrated in Usher et al. (2012),the central GCs would on average appear ‘redder’ than their outskirt counterparts. As a result, a long history of minor mergers would lead us to detect a clear trend in mean colour of a subpopulation associated with distance from the galactic center, i.e a colour gradient. We would clarify that while the above explanation applies to the blue subpopulation, as the red subpopulation is generally associated with in situ formation, any red colour gradient would have to be associated with a different formation scenario. For NGC 4526, measurements of mean colour of each subpopulation stayed consistent with increasing galactocentric radius. This result does not necessarily rule out the presence of a colour gradient, as our result is greatly limited by having to bin our small sample of 56 Chapter 7. Discussion

GCs. We would need additional measurements of GCs and their colours. Additionally an observable quantity that could confirm the presence of a formation history rich in minor mergers is the extent of the blue subpopulation as well as its surface density throughout the galaxy. In galaxies that formed through major mergers the central regions would be dominated by red GCs, which would mean we would observe a higher surface density of red GCs than that of to blue GCs in the innermost regions. However in a minor merger dominated galaxy where there is a much smaller fraction of red GCs to begin, with blue GCs would exhibit a higher projected surface density throughout the galaxy. To infer whether we have a more centrally concentrated red subpopulation, we turn to the surface density profiles and Sersic fits described in Section 5. The Sersic fits to the surface density profiles (see Table 5.1) show that the blue subpopulation has a higher surface density throughout the entire galaxy. The red subpopulation steeply declines and reaches background levels at a smaller radius. This would support the idea that at least part of the blue subpopulation was brought in through minor mergers. We do not see the high central density of red subpopulation GCs, which would further support a two phase/major merger model.

As smaller galaxies are brought in through minor mergers, it is likely for the galaxy’s dark matter mass to increase more rapidly than its stellar mass, as the infalling galaxies would have high fractions of dark matter compared to their stellar mass. The dark matter fraction values published by (Forbes et al., 2016) were preliminary results which were later improved upon by Alabi et al. (2017). Alabi et al. (2017) tested the effects of correcting for the underlying rotation of the galaxy when estimating the galaxy total mass. NGC 4526 was one of the galaxies that was most affected by this correction. Its dark matter fraction changed from 58% to 80%. We repeated the fDM calculations for NGC 4526 using several subsamples of GCs with RV measurements and found that changes in the RV input sample affect the dark matter fraction. Additionally, the more GCs we excluded the higher the associated errors. When we repeated the measurements using the subsample that ex- cluded sources with a velocity dispersion ≤ 300kms−1, as well as underlying substructure, and were positioned more than 1.5 arcmin from the galaxy center, our final result for the 57 dark matter fraction in NGC 4526 was 83%. In Forbes et al. (2016) the scaling relation relied upon Rmax which differed for each galaxy in the sample. There was some concern on our part that using a set Re value would be more suitable. We therefore investigated how the scaling relation behaved when using fDM values at 5 Re and 8 Re. However, while having a consistent Re value for all galaxies would improve our understanding of the scaling relation, this would only be the case if we were to adjust the fb measurement accordingly. As the values are shown in Figure 6.4, the fb is valid for the maximum radius

(i.e full extent of GC system) but the distance to which we measure fb (10.8 arcmin) is different from the value for Rmax (9 arcmin). This is not just the case for NGC 4526 but all galaxies included in the sample. Several galaxies have a large difference between their

Rmax and 8 Re values, which we would expect to also translate into the GC system. It is possible to calculate the fraction of blue GCs for specific radii, given access to the relevant Sersic fit parameters. As, apart from NGC 4526, these were not readily available in the literature we had to stop our investigation there. The original best fit, taken from Forbes et al. (2016), for the scaling relation was fb = 3.06 ( 1.28) × fDM - 2.02 ( 1.11). Using the updated fDM values at Rmax (and including NGC 4526 with the previously named subsample), the new best fit line lay at fb = 2.99 (1.06) × fDM - 2.05 ( 0.95). The spread increased from 0.16 to 0.24. however, even then it is hard to draw conclusions about the proposed scaling relation, as we would need the fb value for all galaxies at a number of different Re. While we can do that for NGC 4526, this is no trivial matter for the rest of the sample, particularly because the information regarding their GC systems surface density function is not always well known (and should be a Sersic rather then a linear fit). As many of the features uncovered by our analysis match those described by Choksi et al. (2018), we propose that NGC 4526 did indeed form through a large number of minor mergers. Those features include the wide spread of colours of the blue subpopulation compared to the much narrower spread of colours in the red subpopulation.

8 Conclusion and proposed further work

In this thesis we studied the GC system of NGC 4526 with the use of Subaru imaging, GC radial velocities sampled by DEIMOS and existing data from ACSVCS (Peng et al., 2008). We improved on the original data-set by being able to cover the entirety of the GC system and we could confirm that the total number of GCs Peng et al. (2008) had estimated matches our calculations. Overall we found the GC system to be bimodal but containing a very large fraction of blue GCs compared to other galaxies in the literature (Kartha et al., 2014). Having re-estimated the galaxy’s dark matter fraction (fDM ) we found it to be strongly dependent on how we defined the GC input sample. Nevertheless the new measurements for fDM and the fraction of blue GCs (fb) are close to the scaling relation proposed by Forbes et al. (2016). In Section 8.1 we propose how to further improve and test this scaling relation given additional data. NGC 4526 has many features that point to a minor merger rich formation history as described by Choksi et al. (2018) such as: a high fraction of blue GCs, a wide spread (σ) of colours contained in the blue subpopulation, the large extent and effective radius of the blue subpopulation and finally the high surface density of blue GCs throughout the galaxy (even in the most central regions). What we did not find was a colour gradient; this may be due to the limitations of our data-set and would need further investigating. While further work is advisable we propose that NGC 4526 is a prime example of a galaxy formed through a series of minor mergers.

59 60 Chapter 8. Conclusion and proposed further work

8.1 Proposed further work

To expand on the work presented here on NGC 4526 and its GC system and galaxy dark matter property relations, we have several recommendations. Firstly a higher number of GC radial velocities need to be measured with DEIMOS or a different instrument. Not only would this improve our dark matter fraction estimates for the galaxy but it also could help us understand the kinematic difference between NGC 4526’s blue and red subpopulations. Currently most of the measured radial velocities are associated with blue

GCs, a bias that we would like to see removed. As mentioned in Section 7, Rmax values differ between galaxies and the radius to which we probe fDM is generally not the same as the radius of fb. In order to fully rule out the possibility that the scaling relation is an artifact from how we define and measure fDM and fb, we suggest that all fDM and fb measurements should be taken at 8 Re (as dark matter content should be dominant from

5 Re and beyond). In order to do this we would need several more observations of GC radial velocities, as well as improved optical imaging with a larger field of view than HST, so that any galaxies that fall short of the 8 Re with current data could be updated.

There is still much to be learned from extra-galactic GC systems. However often the ability to produce testable predictions in simulations as well as the available data are the limiting factors. We would like to further explore how the ratio of the effective radii of the blue and red subpopulation relates to their host galaxy properties. This would require deep wide field imaging similar to our own Subaru images but ideally with supplementary HST data for the most central regions. An existing data-set that would be complementary was used in Pota et al. (2013b); however his results from fitting Sersic profiles to a number of GC systems in the SLUGGS galaxy sample were not made publicly available. Also, to draw any meaningful conclusions we would need to include around 50 or more galaxies from a variety of environments, masses and types. We would further like to investigate how the mean colour spread of a subpopulation (the blue subpopulation in particular) varies with distance from the galaxy center. Small accreted galaxies, which would deposit their GCs in the outskirts, would bring in subpopulations with more peaked colour profiles (smaller value of σ in the fitted Gaussian). We theorise that colour spreads would get increasingly narrow the greater the galactocentric radius. The data sample would have to be comprised 8.1. Proposed further work 61 of galaxies that have large confirmed GC systems and the imaging itself would have to cover the entire galaxy (i.e imaging out to the point where we can clearly determine when GC surface density reaches background levels) and reach faint magnitudes (comparable to those in Peng et al. (2008)). Those requirements would currently restrict us to high mass early type galaxies.

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Table A.1: Full list of GC candidates used during this thesis. Including those objects at high galactocentric radii that were used to determine the full extend of the GC system of NGC 4526.

ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC1 188.4811974 7.7089366 116.859 23.606 0.775

GC2 188.5270169 7.689219 63.427 23.608 0.793

GC3 188.5141561 7.689198 37.157 23.869 1.061

GC4 188.5303283 7.6974296 63.983 23.707 0.934

GC5 188.4901499 7.7102071 88.759 23.777 1.013

GC6 188.5239849 7.6865751 61.709 23.612 0.876

GC7 188.4905993 7.6940782 80.438 23.812 1.094

GC8 188.5183193 7.6926137 32.028 23.705 0.989

GC9 188.5058404 7.685444 55.553 23.647 0.943

GC10 188.5222423 7.7018434 35.863 23.452 0.759

GC11 188.5254304 7.7028016 47.74 23.692 1.008

GC12 188.5119726 7.7042259 17.517 23.656 1.015

GC13 188.488659 7.7079809 90.529 23.667 1.035

GC14 188.512368 7.6916018 28.044 23.675 1.052 Continued on next page

71 72 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC15 188.5177991 7.7055048 29.014 23.343 0.723

GC16 188.515101 7.6969401 12.778 23.679 1.073

GC17 188.4946207 7.7295746 126.033 23.456 0.853

GC18 188.4958751 7.7035594 61.2 23.426 0.824

GC19 188.5122339 7.7060722 24.088 23.463 0.861

GC20 188.5162794 7.7114048 45.326 23.432 0.836

GC21 188.4888439 7.6987889 84.435 23.562 0.967

GC22 188.5080806 7.6962812 19.356 23.484 0.889

GC23 188.5084894 7.6986257 14.592 23.564 0.976

GC24 188.4877836 7.7189401 112.852 23.576 0.988

GC25 188.5254751 7.7032467 48.316 23.593 1.01

GC26 188.5266654 7.6982437 50.694 23.356 0.778

GC27 188.519063 7.7013271 24.417 23.295 0.725

GC28 188.5131612 7.6978768 5.915 23.266 0.703

GC29 188.5267716 7.6935297 55.099 23.34 0.782

GC30 188.5091159 7.6950445 19.777 23.581 1.03

GC31 188.507825 7.6788991 75.639 23.448 0.915

GC32 188.5092383 7.6892568 38.311 23.327 0.81

GC33 188.5172846 7.7040928 24.046 23.356 0.852

GC34 188.357140504 7.70835356324 560.713 23.321 0.823

GC35 188.242831647 7.65056720304 986.838 23.442 0.944

GC36 188.365148467 7.60363527508 632.458 23.132 0.634

GC37 188.298821165 7.78442587209 828.686 23.767 1.269

GC38 188.4915553 7.7164572 96.762 23.603 1.108

GC39 188.169821593 7.52441860174 1385.088 23.57 1.082

GC40 188.564639768 7.83221996034 514.468 23.341 0.853

GC41 188.352750409 7.98179413768 1169.114 23.473 0.987 Continued on next page 73

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC42 188.270037529 7.80060749854 946.648 23.482 0.997

GC43 188.643815372 7.65203804584 501.791 23.133 0.648

GC44 188.35995161 7.77404303644 612.229 23.178 0.702

GC45 188.409836527 7.67046191463 384.13 23.251 0.776

GC46 188.546545347 7.65538215745 199.259 23.288 0.814

GC47 188.575160947 7.89198332048 729.914 23.555 1.081

GC48 188.39978181 7.7567180139 456.085 23.154 0.682

GC49 188.35265363 7.75871976991 614.555 23.559 1.087

GC50 188.487114516 7.57488136071 456.573 23.247 0.776

GC51 188.591112885 7.63853571483 356.947 23.175 0.705

GC52 188.395947896 7.65733966855 446.134 23.203 0.733

GC53 188.685893241 7.58551654103 745.891 23.186 0.717

GC54 188.406564488 7.92469791511 897.367 23.665 1.201

GC55 188.670339409 7.89817591552 914.288 23.348 0.886

GC56 188.279839834 7.67258525701 843.434 23.244 0.784

GC57 188.322707342 7.70195194011 683.762 23.124 0.666

GC58 188.318247324 7.77668593403 753.405 22.928 0.471

GC59 188.305346809 7.97036811459 1228.965 23.64 1.185

GC60 188.522855631 7.5026545371 708.221 22.93 0.478

GC61 188.541321231 7.92356182646 814.584 23.11 0.658

GC62 188.471230659 7.63992488238 260.016 23.109 0.661

GC63 188.479623704 7.56533753286 496.036 23.328 0.882

GC64 188.242612207 7.78322332262 1018.095 23.48 1.035

GC65 188.549558794 7.68115588471 147.865 23.177 0.732

GC66 188.531713 7.6917696 73.814 23.504 1.059

GC67 188.336195297 7.85058542507 837.096 23.01 0.568

GC68 188.5126289 7.6886675 38.604 23.438 0.997 Continued on next page 74 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC69 188.475700243 7.70744696311 136.253 23.468 1.028

GC70 188.441503977 7.53169335482 654.843 23.106 0.668

GC71 188.553468847 7.71587771121 158.954 23.502 1.065

GC72 188.786127426 7.83482496122 1099.169 22.947 0.512

GC73 188.41316779 7.90312742506 817.06 23.069 0.636

GC74 188.5116996 7.7056802 22.844 23.178 0.748

GC75 188.469836708 7.54927093376 561.0 23.011 0.581

GC76 188.587843333 7.86811255278 665.935 23.524 1.095

GC77 188.32484361 7.94770343765 1121.535 23.226 0.798

GC78 188.468565481 7.79386143918 376.152 23.269 0.841

GC79 188.7612385 7.79763170721 962.732 23.223 0.796

GC80 188.52089177 7.85090380893 547.246 23.441 1.015

GC81 188.354711419 7.88771072512 885.505 23.01 0.584

GC82 188.5189267 7.6933199 31.653 23.174 0.749

GC83 188.550674164 7.6817540127 150.58 23.587 1.162

GC84 188.486255157 7.46708865614 840.674 23.399 0.974

GC85 188.364682818 7.85880839748 783.668 23.145 0.725

GC86 188.625212979 7.7520161383 447.843 23.123 0.706

GC87 188.355304145 7.74186794565 586.877 23.305 0.888

GC88 188.350390935 7.50465423223 911.687 23.428 1.012

GC89 188.658120167 7.84219042826 734.616 23.199 0.784

GC90 188.76856273 7.75211575431 940.941 23.439 1.026

GC91 188.321111711 7.84717577818 871.446 23.416 1.003

GC92 188.617603586 7.78308893255 483.969 23.272 0.86

GC93 188.56890193 7.61587871885 361.726 23.221 0.815

GC94 188.499552 7.7052821 50.849 23.41 1.005

GC95 188.372621677 7.87125764889 798.78 23.346 0.941 Continued on next page 75

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC96 188.432391971 7.6410424114 356.547 23.408 1.005

GC97 188.506914899 7.73725566076 138.83 23.224 0.821

GC98 188.462910972 7.75370563595 265.791 23.108 0.707

GC99 188.359109232 7.83551293774 739.269 23.131 0.732

GC100 188.539500267 7.52744277654 625.554 23.071 0.672

GC101 188.510306161 7.63192931475 242.016 22.956 0.558

GC102 188.662453476 7.47008068711 985.291 23.024 0.628

GC103 188.662976726 7.91900828147 958.977 23.235 0.84

GC104 188.23932564 7.70227107053 983.925 23.308 0.912

GC105 188.470562957 7.49656926108 744.722 23.378 0.983

GC106 188.80125429 7.79472877088 1094.61 23.229 0.834

GC107 188.379993925 7.55394132765 707.887 23.216 0.823

GC108 188.294767506 7.86169050589 978.578 23.586 1.193

GC109 188.722905048 7.73380783496 767.259 23.141 0.75

GC110 188.446360542 7.47356584363 846.293 22.999 0.61

GC111 188.28635069 7.47796319507 1139.026 23.241 0.853

GC112 188.5052309 7.7051439 33.204 23.178 0.791

GC113 188.446972402 7.80596956437 451.47 23.171 0.784

GC114 188.612503411 7.90146089327 812.357 22.98 0.594

GC115 188.711328776 7.61530798682 776.374 23.106 0.721

GC116 188.720410555 7.65790118988 762.619 22.964 0.579

GC117 188.498806184 7.53976599951 575.812 23.03 0.647

GC118 188.5268028 7.6917032 58.045 23.381 1.0

GC119 188.336095377 7.75825827139 670.207 23.035 0.654

GC120 188.534038035 7.68763530928 87.484 23.032 0.652

GC121 188.603397466 7.80988164161 515.561 23.025 0.646

GC122 188.5197521 7.6756461 89.312 23.174 0.795 Continued on next page 76 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC123 188.557852735 7.82938946247 496.449 23.089 0.711

GC124 188.744408613 7.79544047056 903.624 23.23 0.853

GC125 188.357009002 7.51137416372 877.853 23.01 0.637

GC126 188.5206388 7.7025077 31.127 23.229 0.857

GC127 188.777719329 7.55227841458 1090.978 22.995 0.623

GC128 188.366627951 7.4856718477 930.948 23.36 0.99

GC129 188.521313943 7.44644554637 910.153 22.823 0.453

GC130 188.793025462 7.65785556358 1020.33 23.017 0.65

GC131 188.243280573 7.63772037968 994.494 22.964 0.598

GC132 188.489595394 7.87658146424 644.23 23.122 0.757

GC133 188.712944391 7.45853664903 1127.026 23.502 1.138

GC134 188.549686976 7.91789133326 798.815 22.963 0.599

GC135 188.683050447 7.51133480267 912.928 23.127 0.764

GC136 188.275177517 7.62685999596 893.496 23.342 0.981

GC137 188.750208336 7.55760601096 995.537 23.147 0.789

GC138 188.515613302 7.83941493957 505.19 23.104 0.748

GC139 188.41264722 7.66370512522 381.812 23.134 0.781

GC140 188.4961818 7.7008406 58.462 23.307 0.955

GC141 188.577533691 7.72120187878 246.845 23.017 0.666

GC142 188.282872642 7.67008930513 833.666 23.244 0.896

GC143 188.518135458 7.428690419 973.735 22.813 0.467

GC144 188.415361233 7.55459985118 627.106 23.055 0.71

GC145 188.5026968 7.7095439 50.612 23.341 1.001

GC146 188.760692097 7.60665105071 953.08 22.936 0.597

GC147 188.5189605 7.6971753 24.375 23.425 1.087

GC148 188.677515319 7.6428544224 627.226 22.848 0.511

GC149 188.342105571 7.82223075884 757.134 23.169 0.834 Continued on next page 77

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC150 188.60190974 7.47714697656 861.318 22.922 0.589

GC151 188.524760266 7.51453378152 665.932 23.434 1.101

GC152 188.740053617 7.81155354934 913.352 23.015 0.684

GC153 188.562422284 7.64119894174 274.986 23.043 0.713

GC154 188.5125654 7.6894168 35.906 23.316 0.988

GC155 188.801091995 7.83189013111 1143.22 23.104 0.777

GC156 188.793506269 7.86930893205 1182.328 23.292 0.966

GC157 188.553515786 7.72062016096 166.337 22.941 0.619

GC158 188.48788908 7.73519680085 157.474 23.064 0.744

GC159 188.5016226 7.7043888 42.805 23.353 1.034

GC160 188.525760371 7.64793066111 190.243 23.033 0.715

GC161 188.751257493 7.82623933307 973.387 22.983 0.665

GC162 188.61579659 7.67320212369 382.972 23.145 0.827

GC163 188.264468244 7.62402319015 933.353 22.977 0.661

GC164 188.575685979 7.80596320476 446.655 22.826 0.513

GC165 188.730641347 7.48376278751 1103.213 23.072 0.76

GC166 188.41570144 7.92543776255 886.322 22.854 0.545

GC167 188.766043086 7.69129169325 912.758 23.064 0.756

GC168 188.488980574 7.91681901465 788.337 22.909 0.604

GC169 188.421105117 7.68923213924 331.37 23.027 0.724

GC170 188.638808499 7.6066127913 563.264 22.867 0.57

GC171 188.449849592 7.5698916039 517.188 22.818 0.523

GC172 188.49384699 7.81075203907 407.532 23.128 0.839

GC173 188.628420291 7.87773980837 766.353 23.165 0.88

GC174 188.203800794 7.51426070017 1295.704 23.213 0.929

GC175 188.643417623 7.53383456738 758.789 23.052 0.768

GC176 188.286081117 7.92820111496 1159.847 23.272 0.989 Continued on next page 78 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC177 188.404941168 7.51254843816 775.483 23.044 0.763

GC178 188.254439096 7.67240846401 934.411 22.987 0.71

GC179 188.494934202 7.59950906032 364.194 23.364 1.089

GC180 188.386668381 7.56646492632 658.516 23.033 0.769

GC181 188.732223944 7.66516178392 799.968 22.93 0.668

GC182 188.284307291 7.86749556588 1021.273 23.128 0.872

GC183 188.530012731 7.68285136063 85.726 22.908 0.653

GC184 188.324038662 7.79666884134 764.349 23.387 1.132

GC185 188.486514121 7.76525345289 255.971 23.073 0.819

GC186 188.348484367 7.50656516559 910.853 22.708 0.453

GC187 188.467594651 7.63061095046 295.118 23.127 0.876

GC188 188.267028267 7.82560756078 994.508 22.99 0.739

GC189 188.4965334 7.7038135 59.158 23.13 0.881

GC190 188.505597212 7.69830151836 25.452 22.875 0.625

GC191 188.499367023 7.72247443437 96.682 23.328 1.08

GC192 188.474306554 7.70262981783 138.507 23.008 0.762

GC193 188.627505576 7.66185032065 434.803 22.934 0.689

GC194 188.745212624 7.84042392079 979.75 22.937 0.693

GC195 188.642680105 7.78061606482 552.549 23.306 1.062

GC196 188.750112131 7.54839756581 1012.609 23.154 0.91

GC197 188.631663019 7.65402599087 458.267 22.755 0.522

GC198 188.594639144 7.70616945236 296.359 23.157 0.926

GC199 188.651301757 7.71323957016 501.837 23.362 1.151

GC200 188.489121697 7.7101826347 93.506 22.713 0.505

GC201 188.810812818 7.64611114983 1090.322 23.062 0.859

GC202 188.712144103 7.5234120357 957.098 22.928 0.726

GC203 188.491086 7.6945292 78.378 23.147 0.948 Continued on next page 79

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC204 188.294295287 7.89635030555 1059.202 22.772 0.577

GC205 188.416269085 7.85659374869 664.614 23.014 0.819

GC206 188.448934985 7.7825338252 377.816 22.902 0.707

GC207 188.283765059 7.89004565433 1072.949 23.303 1.109

GC208 188.551621616 7.75141770209 234.873 23.229 1.036

GC209 188.658043611 7.57042594371 699.079 23.155 0.966

GC210 188.314593594 7.88042110296 966.558 22.709 0.525

GC211 188.392681227 7.81027682811 588.707 23.205 1.022

GC212 188.750608227 7.80945799622 944.365 23.219 1.039

GC213 188.55198561 7.8876145673 693.234 23.17 0.991

GC214 188.550323338 7.67803473515 155.509 22.706 0.529

GC215 188.311950037 7.59906593419 807.223 22.919 0.742

GC216 188.4024032 7.98031096489 1087.285 23.033 0.86

GC217 188.4892825 7.683712 100.259 22.985 0.819

GC218 188.507227 7.7138491 55.375 22.908 0.746

GC219 188.5231698 7.6904186 49.91 23.068 0.908

GC220 188.5213718 7.6926201 39.94 23.069 0.91

GC221 188.557560836 7.70663554325 164.036 22.796 0.64

GC222 188.5176963 7.6963591 21.507 23.222 1.067

GC223 188.5113296 7.7057255 23.202 23.2 1.045

GC224 188.548760602 7.92524470891 824.394 22.801 0.653

GC225 188.4902269 7.7034484 80.823 22.889 0.745

GC226 188.547332452 7.45071338052 902.939 22.67 0.529

GC227 188.463773316 7.92093182214 817.669 22.885 0.745

GC228 188.412004905 7.77631558911 456.55 22.717 0.577

GC229 188.26164698 7.52301053041 1103.743 23.087 0.948

GC230 188.463088035 7.67431970462 199.411 23.114 0.979 Continued on next page 80 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC231 188.334844652 7.5603656755 811.844 23.107 0.972

GC232 188.734632019 7.77376999951 843.219 22.966 0.835

GC233 188.411640434 7.51172402193 766.324 22.844 0.717

GC234 188.448316352 7.63148754377 335.951 23.0 0.874

GC235 188.261213018 7.56411987083 1027.291 23.03 0.906

GC236 188.632591401 7.40415131475 1146.347 22.835 0.713

GC237 188.5241774 7.6919961 49.424 22.912 0.794

GC238 188.5143649 7.7033147 15.613 23.146 1.028

GC239 188.215584028 7.64135525721 1089.36 22.75 0.633

GC240 188.581385239 7.56426698365 544.933 23.029 0.913

GC241 188.47900891 7.43029699089 975.284 22.633 0.519

GC242 188.658029859 7.43584364227 1082.735 23.047 0.935

GC243 188.595971427 7.59155714162 489.87 22.644 0.532

GC244 188.334020926 7.73510927834 655.883 22.571 0.46

GC245 188.421850494 7.92562098936 878.455 23.068 0.959

GC246 188.596374031 7.58061117424 522.412 23.119 1.019

GC247 188.464083591 7.68856142935 178.815 22.928 0.828

GC248 188.794037502 7.74750029099 1027.968 22.801 0.701

GC249 188.4989437 7.7268016 109.917 22.914 0.815

GC250 188.781375854 7.58822776007 1046.641 22.911 0.814

GC251 188.5216418 7.65017709931 179.149 23.005 0.909

GC252 188.539913698 7.70641959429 101.714 22.874 0.782

GC253 188.603602657 7.40672934064 1102.376 23.044 0.953

GC254 188.537270985 7.54570797269 559.354 22.689 0.598

GC255 188.78525273 7.67935404179 984.053 22.615 0.525

GC256 188.279866247 7.84524966438 989.373 22.88 0.792

GC257 188.244642219 7.63165847033 994.816 22.642 0.554 Continued on next page 81

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC258 188.607692661 7.61628137791 453.949 22.942 0.855

GC259 188.717137223 7.59069848389 833.318 23.085 1.002

GC260 188.562872669 7.73896099973 230.876 22.71 0.63

GC261 188.645369934 7.78416694015 567.57 22.823 0.745

GC262 188.563765797 7.68243692292 193.668 23.097 1.02

GC263 188.446967202 7.77603932773 364.071 22.77 0.693

GC264 188.465573381 7.70341153254 170.072 22.963 0.887

GC265 188.315915991 7.50017585876 1007.162 22.67 0.595

GC266 188.708697216 7.84046160835 870.164 22.639 0.567

GC267 188.630279228 7.60211727282 548.956 23.124 1.053

GC268 188.386752885 7.49721204697 856.526 22.664 0.594

GC269 188.4996103 7.7012958 46.524 23.033 0.964

GC270 188.692936431 7.73125526496 659.37 22.777 0.71

GC271 188.325633044 7.58729793952 784.333 22.726 0.665

GC272 188.366608519 7.89561159248 881.3 23.184 1.122

GC273 188.500834616 7.70388288105 45.766 22.906 0.853

GC274 188.587788156 7.58494215985 492.108 22.63 0.579

GC275 188.182409161 7.55756412698 1293.378 22.926 0.875

GC276 188.367157189 7.74409762151 548.132 22.526 0.476

GC277 188.646525338 7.44850935935 1022.897 22.858 0.809

GC278 188.317695261 7.94672729859 1134.468 22.856 0.808

GC279 188.808628168 7.64945741692 1080.521 22.787 0.742

GC280 188.535976 7.7059211 86.995 22.983 0.942

GC281 188.5005691 7.7031866 44.734 22.846 0.807

GC282 188.557294126 7.68594374671 167.672 22.558 0.522

GC283 188.5010959 7.7207352 86.97 23.0 0.966

GC284 188.4863862 7.7199658 119.063 22.857 0.827 Continued on next page 82 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC285 188.774700637 7.86692247233 1120.319 23.022 0.992

GC286 188.527678995 7.50120145861 714.553 22.66 0.634

GC287 188.5158801 7.6883974 41.375 22.948 0.923

GC288 188.289846108 7.77816469189 850.978 22.658 0.634

GC289 188.192614495 7.71640686045 1153.7 22.767 0.749

GC290 188.5119575 7.7075195 29.344 22.762 0.745

GC291 188.614266836 7.64439181293 415.591 22.593 0.578

GC292 188.365989336 7.47730091607 957.238 22.465 0.451

GC293 188.314412353 7.90054364657 1017.338 22.666 0.657

GC294 188.219710404 7.65287103701 1067.536 22.792 0.786

GC295 188.227618227 7.79948699949 1087.774 22.508 0.504

GC296 188.468445228 7.74101783016 219.191 22.895 0.896

GC297 188.511455055 7.52161104292 639.032 23.014 1.015

GC298 188.5333372 7.6998721 74.369 23.0 1.002

GC299 188.462227581 7.7436997807 242.221 22.63 0.635

GC300 188.568301264 7.40935823475 1062.214 22.472 0.482

GC301 188.4907329 7.7249728 120.491 22.964 0.978

GC302 188.583676749 7.46042118584 896.569 22.954 0.974

GC303 188.274910949 7.62929141716 891.907 22.721 0.746

GC304 188.669105181 7.81821188063 707.942 22.649 0.674

GC305 188.479773015 7.66313793796 175.382 22.456 0.482

GC306 188.697722217 7.43877271623 1149.982 22.828 0.868

GC307 188.336536454 7.92728938046 1037.577 22.512 0.56

GC308 188.287783414 7.92819804644 1155.538 23.084 1.135

GC309 188.334840792 7.78144562881 705.302 22.42 0.471

GC310 188.632809672 7.92764292241 929.539 22.46 0.515

GC311 188.675878832 7.40466017367 1212.071 22.586 0.643 Continued on next page 83

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC312 188.444444236 7.79785166896 431.949 22.44 0.498

GC313 188.606075305 7.77986324652 444.625 22.731 0.79

GC314 188.5187614 7.6998079 22.373 22.763 0.823

GC315 188.444203008 7.57844403951 499.384 22.939 1.006

GC316 188.506449166 7.94499827523 885.454 22.979 1.047

GC317 188.412488304 7.65535278699 393.399 22.736 0.805

GC318 188.4918309 7.7129531 88.465 22.898 0.968

GC319 188.232115323 7.81687980867 1095.2 22.549 0.621

GC320 188.712472281 7.70108312518 719.503 22.844 0.919

GC321 188.5183639 7.6941946 28.045 22.702 0.778

GC322 188.780284935 7.53598499143 1128.451 22.856 0.935

GC323 188.42916697 7.82157663823 533.492 22.693 0.772

GC324 188.369644793 7.50677015514 862.791 22.856 0.936

GC325 188.553818655 7.73926061915 207.083 22.666 0.749

GC326 188.50101435 7.91001740992 760.393 22.605 0.689

GC327 188.556580597 7.68869869211 162.641 22.967 1.051

GC328 188.514213431 7.8306307943 473.487 22.827 0.911

GC329 188.515206063 7.73179522842 118.012 22.816 0.901

GC330 188.591873651 7.76648288853 374.459 22.781 0.867

GC331 188.828435637 7.73640788465 1144.835 22.708 0.798

GC332 188.540559619 7.69115962067 104.582 22.741 0.832

GC333 188.65189293 7.74791431184 531.268 22.854 0.946

GC334 188.4732791 7.6933603 141.62 22.749 0.845

GC335 188.5137851 7.6970016 9.732 22.81 0.907

GC336 188.433583395 7.50823676289 743.744 22.587 0.684

GC337 188.795453305 7.72036871371 1021.07 22.967 1.067

GC338 188.817715941 7.7791086799 1135.47 22.443 0.546 Continued on next page 84 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC339 188.573924719 7.66351168433 255.218 22.648 0.754

GC340 188.501621547 7.66928204334 114.469 22.923 1.03

GC341 188.160999005 7.54017232582 1389.154 22.98 1.089

GC342 188.485706157 7.56883379568 478.92 22.62 0.73

GC343 188.501967121 7.50802354194 689.002 22.833 0.944

GC344 188.591826811 7.67547307518 297.577 22.536 0.652

GC345 188.5327074 7.6959619 73.132 22.652 0.768

GC346 188.779605044 7.52400806037 1149.431 22.837 0.959

GC347 188.5253675 7.6883892 60.621 22.869 0.992

GC348 188.336942038 7.61691235126 698.254 22.455 0.579

GC349 188.5114692 7.6866261 46.103 22.652 0.78

GC350 188.477795394 7.62495162845 294.961 22.402 0.532

GC351 188.52116 7.690603 44.216 22.65 0.782

GC352 188.4993492 7.6956046 48.882 22.648 0.78

GC353 188.641513648 7.85237181885 720.907 22.341 0.474

GC354 188.4905645 7.7190378 105.515 22.648 0.783

GC355 188.547848065 7.76761576861 277.298 22.549 0.685

GC356 188.590613561 7.64814909866 335.412 22.625 0.762

GC357 188.436538213 7.92233087102 848.968 22.87 1.01

GC358 188.735333967 7.68261035765 803.969 23.137 1.28

GC359 188.527249 7.704321 55.53 22.63 0.774

GC360 188.263042865 7.91605868951 1190.467 23.141 1.286

GC361 188.523142273 7.48745623384 762.917 22.397 0.543

GC362 188.598690895 7.55720916758 597.489 22.33 0.479

GC363 188.695736912 7.57303802136 800.363 22.617 0.767

GC364 188.4990177 7.7120602 66.31 22.874 1.026

GC365 188.5065684 7.7061746 32.338 22.853 1.006 Continued on next page 85

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC366 188.656490173 7.55409794282 735.396 22.909 1.067

GC367 188.5232744 7.7016614 39.279 22.864 1.022

GC368 188.389888702 7.80409571008 581.416 22.927 1.086

GC369 188.768677434 7.73985705453 933.402 22.577 0.736

GC370 188.661039345 7.57181064769 703.936 22.373 0.534

GC371 188.21046738 7.49466827535 1313.36 22.902 1.069

GC372 188.599440116 7.59433857846 489.866 22.861 1.036

GC373 188.654842333 7.64196152528 551.797 22.92 1.104

GC374 188.414226316 7.74974555827 398.36 22.893 1.077

GC375 188.5074523 7.7132067 52.934 22.862 1.047

GC376 188.40086957 7.45044548766 981.456 22.651 0.836

GC377 188.577381065 7.70712361616 234.915 22.515 0.702

GC378 188.403854538 7.90178212177 828.027 22.78 0.969

GC379 188.242982846 7.48704086247 1234.963 22.777 0.974

GC380 188.505889469 7.55972790862 502.382 22.513 0.717

GC381 188.153956396 7.52929757136 1428.606 22.652 0.859

GC382 188.5092542 7.7084569 34.653 22.818 1.027

GC383 188.565701735 7.47930902499 814.053 22.423 0.633

GC384 188.443901547 7.58010249311 494.741 22.441 0.652

GC385 188.342562823 7.97308410145 1160.842 22.796 1.012

GC386 188.236787404 7.74037996937 1004.047 22.34 0.557

GC387 188.546942273 7.65068609592 213.692 22.486 0.704

GC388 188.528788631 7.77087926659 264.823 22.609 0.828

GC389 188.731458194 7.45328950327 1184.837 22.522 0.746

GC390 188.555161049 7.89457777049 720.135 22.573 0.803

GC391 188.674476548 7.7597252837 622.196 22.315 0.547

GC392 188.49803631 7.64495089482 201.939 22.517 0.749 Continued on next page 86 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC393 188.329691898 7.89314992675 960.004 22.529 0.765

GC394 188.73533624 7.56176273422 941.993 22.888 1.127

GC395 188.420371261 7.52342358141 714.378 22.799 1.041

GC396 188.227396641 7.49470318486 1263.272 22.207 0.451

GC397 188.669468424 7.4547229826 1045.423 22.279 0.535

GC398 188.591410619 7.81585951472 507.038 22.572 0.83

GC399 188.207995218 7.76674814769 1123.352 22.356 0.615

GC400 188.430290334 7.80649876712 487.121 22.412 0.675

GC401 188.67683898 7.91059956184 963.92 22.335 0.598

GC402 188.566317379 7.7409845027 245.127 22.461 0.727

GC403 188.253694664 7.55389051864 1068.738 22.496 0.763

GC404 188.338861 7.88512202239 916.342 22.498 0.768

GC405 188.308085246 7.91612157029 1073.535 22.204 0.479

GC406 188.463678879 7.92576628596 834.746 22.673 0.948

GC407 188.367779473 7.55891811082 725.686 22.523 0.814

GC408 188.739211325 7.49768457724 1091.449 22.322 0.615

GC409 188.70964363 7.42996211669 1200.816 22.281 0.578

GC410 188.241729673 7.88883479241 1190.587 22.582 0.88

GC411 188.491518311 7.52414170437 634.472 22.579 0.877

GC412 188.462942942 7.69764878924 178.915 22.332 0.631

GC413 188.453104002 7.63283828601 320.681 22.376 0.682

GC414 188.5052586 7.7078739 40.019 22.717 1.025

GC415 188.269961677 7.74034797051 886.091 22.22 0.53

GC416 188.665089738 7.7442535963 572.439 22.268 0.582

GC417 188.198050438 7.51930217303 1304.408 22.594 0.911

GC418 188.290401178 7.66103805995 811.648 22.242 0.562

GC419 188.674108992 7.86336128951 829.213 22.276 0.604 Continued on next page 87

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC420 188.445651981 7.75790484895 320.801 22.425 0.756

GC421 188.254578771 7.59971010884 995.495 22.14 0.476

GC422 188.694492614 7.51588707952 929.404 22.357 0.693

GC423 188.4952154 7.7095822 71.778 22.447 0.784

GC424 188.591673867 7.74468871401 328.496 22.542 0.889

GC425 188.49317915 7.66763286305 133.207 22.554 0.902

GC426 188.573993678 7.68014659775 231.258 22.684 1.033

GC427 188.338879986 7.57466117072 769.379 22.639 0.99

GC428 188.54224767 7.69637142893 107.116 22.303 0.663

GC429 188.5067397 7.7182515 70.967 22.551 0.916

GC430 188.5196287 7.7022479 27.417 22.71 1.081

GC431 188.792280518 7.88803952416 1214.977 22.636 1.011

GC432 188.5071053 7.7110332 46.142 22.4 0.785

GC433 188.4977972 7.7019974 53.301 22.38 0.774

GC434 188.5168575 7.6995021 15.549 22.369 0.765

GC435 188.4980285 7.6871288 67.932 22.576 0.973

GC436 188.5131623 7.7141974 53.375 22.486 0.892

GC437 188.599113593 7.89307170252 764.522 22.251 0.662

GC438 188.539927227 7.72409169284 133.222 22.436 0.854

GC439 188.734389867 7.47743469771 1128.843 22.475 0.894

GC440 188.5249839 7.6920083 51.843 22.572 0.992

GC441 188.53873659 7.69817675173 94.081 22.276 0.697

GC442 188.279483028 7.91764504591 1150.355 22.206 0.628

GC443 188.474664243 7.65411489117 211.934 22.221 0.653

GC444 188.5105657 7.705521 23.141 22.343 0.78

GC445 188.437845115 7.97045590909 1013.235 22.365 0.804

GC446 188.458836782 7.65229895376 256.7 22.577 1.022 Continued on next page 88 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC447 188.829686291 7.71815234381 1143.494 22.482 0.93

GC448 188.518703 7.6873167 48.776 22.31 0.759

GC449 188.275407957 7.69117817638 854.441 22.217 0.667

GC450 188.471236148 7.69599530226 149.405 22.305 0.757

GC451 188.8119283 7.74959425604 1092.726 22.316 0.769

GC452 188.596170547 7.4665071693 889.773 22.304 0.758

GC453 188.184922876 7.65454592869 1190.571 22.018 0.473

GC454 188.453567753 7.74540016937 270.105 22.397 0.856

GC455 188.633277718 7.8614874142 728.257 22.786 1.254

GC456 188.541262476 7.54932606082 549.014 22.31 0.787

GC457 188.472094979 7.91085443181 776.093 22.329 0.807

GC458 188.315074967 7.93596699031 1110.309 22.36 0.838

GC459 188.702127182 7.56707680664 831.494 22.181 0.659

GC460 188.594604541 7.78735758898 433.617 22.51 0.991

GC461 188.526470203 7.74821774337 183.663 22.238 0.72

GC462 188.466700633 7.54681726718 572.655 22.188 0.671

GC463 188.462754603 7.72718442239 206.0 22.153 0.638

GC464 188.543381014 7.6905625092 114.941 22.195 0.681

GC465 188.73616205 7.87949655387 1034.072 22.298 0.789

GC466 188.297442625 7.93398450923 1146.725 22.514 1.007

GC467 188.636187527 7.79052507351 553.33 22.304 0.804

GC468 188.295479178 7.67026424343 788.577 22.427 0.936

GC469 188.365685761 7.6637518635 544.068 22.345 0.855

GC470 188.405915944 7.56466694816 617.926 22.087 0.598

GC471 188.754513386 7.78009336567 918.316 22.223 0.734

GC472 188.263004883 7.87339041663 1095.957 22.674 1.187

GC473 188.494731295 7.44931408119 901.591 22.373 0.887 Continued on next page 89

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC474 188.329661142 7.74737414248 681.178 22.045 0.561

GC475 188.667358304 7.76298130533 602.639 22.383 0.907

GC476 188.393812803 7.97807068388 1091.523 22.335 0.866

GC477 188.249857922 7.79670696042 1009.079 22.226 0.767

GC478 188.639238851 7.68169470019 460.122 22.046 0.589

GC479 188.707703309 7.71955818439 706.145 22.136 0.682

GC480 188.614471579 7.4344651842 1020.864 22.195 0.752

GC481 188.207688792 7.57844729754 1180.581 22.435 0.992

GC482 188.5504859 7.71522971311 148.147 22.143 0.701

GC483 188.766486986 7.81521033657 1004.95 22.075 0.636

GC484 188.477800425 7.77221228726 291.476 22.107 0.671

GC485 188.350505426 7.49726317516 932.016 22.227 0.792

GC486 188.54670311 7.72229150879 148.377 21.895 0.465

GC487 188.502274907 7.81095020689 404.321 22.133 0.704

GC488 188.5264215 7.6940292 53.271 22.459 1.032

GC489 188.283143156 7.67041151634 832.555 22.235 0.81

GC490 188.772812336 7.78347160637 984.69 21.918 0.495

GC491 188.745523619 7.5931024549 921.227 22.178 0.756

GC492 188.570326949 7.69393641871 208.58 22.348 0.927

GC493 188.5024791 7.7005792 36.03 22.212 0.794

GC494 188.5190244 7.7009545 23.95 22.364 0.946

GC495 188.613729786 7.54460348416 664.757 22.122 0.713

GC496 188.51034061 7.8683354604 609.245 22.618 1.21

GC497 188.4966801 7.6954526 58.222 22.309 0.904

GC498 188.5095458 7.6731515 95.067 22.194 0.789

GC499 188.32064637 7.73101251302 700.579 22.253 0.85

GC500 188.502584135 7.60599500216 337.178 21.997 0.601 Continued on next page 90 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC501 188.679578251 7.5670458485 766.366 22.11 0.718

GC502 188.5225936 7.6932601 42.233 22.336 0.947

GC503 188.21758681 7.78921047856 1110.538 22.182 0.793

GC504 188.4894509 7.7197536 110.179 22.364 0.981

GC505 188.602806579 7.76487895575 401.821 22.042 0.661

GC506 188.723521034 7.6382715868 790.209 22.038 0.663

GC507 188.636331364 7.74423551042 474.057 22.026 0.653

GC508 188.514222021 7.70968650973 38.488 22.314 0.943

GC509 188.445847153 7.58246745449 483.869 21.846 0.475

GC510 188.580504247 7.48090220821 822.705 21.985 0.617

GC511 188.522912632 7.64745552605 189.634 21.973 0.612

GC512 188.5165055 7.7133241 52.171 22.143 0.788

GC513 188.643849519 7.70337970111 472.676 22.077 0.724

GC514 188.35202296 7.90712812617 946.056 22.098 0.75

GC515 188.20305617 7.54610281149 1243.136 21.88 0.544

GC516 188.4937377 7.7086152 74.743 22.321 0.987

GC517 188.4932971 7.6997625 68.535 22.069 0.735

GC518 188.508319283 7.70560368244 28.021 22.193 0.862

GC519 188.240462267 7.76839921499 1011.017 22.186 0.87

GC520 188.379457587 7.63446391481 532.899 21.779 0.468

GC521 188.43062672 7.65516612123 334.907 22.137 0.831

GC522 188.439843791 7.95486293601 957.24 21.789 0.484

GC523 188.721422123 7.47537074913 1101.743 22.075 0.778

GC524 188.5098164 7.7143794 54.829 22.052 0.762

GC525 188.380105305 7.87059166013 780.162 21.961 0.678

GC526 188.508638885 7.80587195936 384.588 22.475 1.195

GC527 188.367171494 7.69904572131 523.615 22.142 0.867 Continued on next page 91

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC528 188.479499251 7.54656070156 561.994 21.864 0.589

GC529 188.759430531 7.77531836627 929.903 21.999 0.73

GC530 188.555747274 7.88438725156 684.808 22.054 0.789

GC531 188.786997071 7.79114775484 1041.841 21.919 0.661

GC532 188.806780966 7.70245128727 1059.048 21.85 0.594

GC533 188.42625826 7.74336508511 349.336 21.765 0.513

GC534 188.630385271 7.86494438372 732.205 21.735 0.487

GC535 188.850392721 7.79640556828 1265.417 22.074 0.828

GC536 188.4904395 7.7194932 106.949 22.03 0.793

GC537 188.4969274 7.7097289 66.89 22.213 0.986

GC538 188.561604258 7.7223077388 195.109 21.884 0.658

GC539 188.478048251 7.49173117949 756.889 22.184 0.958

GC540 188.674840707 7.60842351391 669.065 21.962 0.736

GC541 188.78098089 7.58614438505 1048.213 21.699 0.476

GC542 188.473000742 7.46226945148 864.495 21.964 0.745

GC543 188.459168384 7.68007742181 204.268 22.192 0.974

GC544 188.541012488 7.64178227386 230.324 21.904 0.691

GC545 188.579115293 7.5832045331 481.07 21.916 0.704

GC546 188.4989286 7.74874225091 185.328 21.921 0.728

GC547 188.712738467 7.58157640764 835.503 22.084 0.894

GC548 188.4755677 7.6927358 133.935 21.973 0.787

GC549 188.350552238 7.77994608175 651.982 22.253 1.067

GC550 188.527601619 7.66882429263 121.659 21.934 0.748

GC551 188.64151843 7.83352321701 670.413 21.913 0.736

GC552 188.740734861 7.45025541526 1215.33 21.871 0.694

GC553 188.5215204 7.6848375 61.474 21.919 0.745

GC554 188.268479262 7.62441379477 919.13 21.83 0.658 Continued on next page 92 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC555 188.503762253 7.49949692967 719.337 21.692 0.522

GC556 188.576832086 7.55014384665 583.999 22.067 0.898

GC557 188.530312026 7.6800903584 93.53 22.124 0.961

GC558 188.561590138 7.42492812467 1002.696 21.909 0.75

GC559 188.5130071 7.7025599 11.559 22.058 0.915

GC560 188.283932102 7.89653499067 1087.607 21.65 0.507

GC561 188.617975357 7.84338764561 643.122 21.775 0.634

GC562 188.271045994 7.86242925069 1049.751 22.029 0.917

GC563 188.254841532 7.52441445096 1121.043 22.063 0.952

GC564 188.366818839 7.90310213051 902.647 21.87 0.761

GC565 188.400601636 7.63379284704 466.825 21.607 0.504

GC566 188.5256117 7.7010457 47.139 21.918 0.815

GC567 188.316288058 7.62298833137 758.069 21.607 0.506

GC568 188.677570142 7.73745164093 609.647 21.618 0.524

GC569 188.211541131 7.69371490854 1084.058 21.636 0.548

GC570 188.4839996 7.7080877 106.426 21.871 0.787

GC571 188.443875453 7.71025063605 250.706 21.882 0.798

GC572 188.740966265 7.89267303376 1077.635 21.808 0.725

GC573 188.547694241 7.70220876052 126.757 21.751 0.675

GC574 188.447658692 7.63577800584 326.623 21.829 0.753

GC575 188.291749899 7.5608686254 938.047 21.767 0.691

GC576 188.255181077 7.63949058778 951.313 21.616 0.543

GC577 188.267735839 7.85629263879 1047.549 21.832 0.767

GC578 188.324419224 7.64273207269 707.276 21.598 0.534

GC579 188.4864307 7.7033627 94.109 21.827 0.773

GC580 188.5329838 7.6942102 75.414 21.833 0.779

GC581 188.420669125 7.70375797027 331.445 21.764 0.713 Continued on next page 93

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC582 188.546552568 7.56616273774 493.975 21.908 0.871

GC583 188.442046067 7.77758835139 379.942 21.481 0.455

GC584 188.266029906 7.80721770848 969.28 21.557 0.534

GC585 188.301820388 7.81532799205 866.559 21.695 0.673

GC586 188.485171697 7.70715466258 102.964 21.611 0.605

GC587 188.602244377 7.93471377682 907.448 21.456 0.453

GC588 188.473640009 7.73021099504 179.507 21.827 0.83

GC589 188.654207571 7.76227044126 558.122 21.502 0.514

GC590 188.696226214 7.61878307329 721.48 21.798 0.818

GC591 188.562410464 7.8221771748 477.907 21.884 0.906

GC592 188.754639869 7.68509134102 872.733 21.889 0.914

GC593 188.491810737 7.66673777533 138.56 21.71 0.746

GC594 188.498849365 7.56257235856 494.051 22.076 1.116

GC595 188.493461144 7.50718357527 694.391 21.974 1.022

GC596 188.42717781 7.85321888032 634.336 21.485 0.537

GC597 188.468282442 7.89681395826 729.391 21.519 0.573

GC598 188.360843672 7.54744098937 772.462 21.619 0.676

GC599 188.5125517 7.7022732 10.386 21.952 1.032

GC600 188.472641516 7.71883087129 160.471 21.622 0.704

GC601 188.5055759 7.6878729 48.276 21.666 0.751

GC602 188.565647788 7.64370133709 276.117 21.66 0.745

GC603 188.49718134 7.80057315529 369.45 21.463 0.572

GC604 188.431307246 7.7320654902 315.846 21.59 0.713

GC605 188.439799177 7.46940766297 867.51 21.362 0.492

GC606 188.380275349 7.87620691253 795.887 21.642 0.793

GC607 188.676560978 7.91444547004 974.286 21.398 0.551

GC608 188.488768337 7.85965218777 584.274 21.453 0.612 Continued on next page 94 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC609 188.211413704 7.69324443506 1084.549 21.472 0.634

GC610 188.175687841 7.51418969651 1383.642 21.476 0.653

GC611 188.21207246 7.57854654904 1165.788 21.869 1.047

GC612 188.440142197 7.68343628188 266.956 21.625 0.808

GC613 188.751741493 7.71811995379 863.552 22.057 1.245

GC614 188.206910502 7.55283610762 1220.056 21.514 0.724

GC615 188.5023583 7.7001417 36.302 21.778 0.994

GC616 188.260410284 7.48949687562 1180.613 21.434 0.67

GC617 188.675594022 7.71752911134 590.439 21.409 0.658

GC618 188.340954843 7.78520326289 691.349 21.551 0.802

GC619 188.294885266 7.797603193 860.303 21.397 0.654

GC620 188.286944398 7.79413903321 881.514 21.217 0.489

GC621 188.442272358 7.89467112611 748.164 21.294 0.583

GC622 188.5228675 7.6963127 38.605 21.46 0.767

GC623 188.5278894 7.7223689 99.309 21.448 0.757

GC624 188.774049216 7.81321437185 1026.875 21.124 0.48

GC625 188.4762368 7.6994732 129.401 21.578 0.935

GC626 188.322935953 7.69249067231 683.279 21.24 0.604

GC627 188.202860958 7.5527056555 1233.423 21.498 0.864

GC628 188.545458815 7.83903905576 517.409 21.328 0.696

GC629 188.487669651 7.77976711512 303.92 21.162 0.533

GC630 188.591750414 7.59429323488 472.814 21.327 0.699

GC631 188.580885162 7.67385214804 262.045 21.327 0.712

GC632 188.5262835 7.6924598 55.129 21.421 0.811

GC633 188.562301488 7.8629985428 616.491 21.501 0.891

GC634 188.309725523 7.66208223009 742.488 21.206 0.603

GC635 188.716414935 7.80835447981 832.413 21.208 0.608 Continued on next page 95

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC636 188.424497402 7.76688227792 400.197 21.299 0.701

GC637 188.724380273 7.78088895599 817.202 21.095 0.5

GC638 188.676188482 7.68771927169 590.274 21.253 0.659

GC639 188.417660943 7.55551276665 619.777 21.331 0.745

GC640 188.736241389 7.87171984685 1016.951 21.448 0.862

GC641 188.4920255 7.7295539 130.908 21.518 0.936

GC642 188.736423078 7.78416060557 861.9 21.382 0.809

GC643 188.812789275 7.66102708963 1089.274 21.407 0.868

GC644 188.631923315 7.80820333824 581.969 21.644 1.113

GC645 188.523849297 7.57415587526 451.67 21.107 0.588

GC646 188.524956576 7.62678659379 264.147 21.266 0.789

GC647 188.600802313 7.62859758608 406.481 21.275 0.816

GC648 188.491889 7.7213814 108.079 21.325 0.885

GC649 188.528634826 7.49789766805 726.677 20.911 0.502

GC650 188.732672852 7.53367897668 991.097 21.015 0.619

GC651 188.490682966 7.69156714134 83.519 21.241 0.846

GC652 188.262413725 7.75630023084 923.968 21.19 0.806

GC653 188.337253462 7.97823635633 1186.698 21.23 0.849

GC654 188.450129244 7.70444985197 225.785 20.87 0.494

GC655 188.318002455 7.84410587248 873.681 20.903 0.529

GC656 188.426073545 7.78313436803 434.234 21.064 0.69

GC657 188.517441976 7.94607574331 889.224 21.345 0.977

GC658 188.572962918 7.69317452038 218.285 21.069 0.704

GC659 188.335154884 7.64311456365 669.929 21.352 0.993

GC660 188.369075492 7.8638868468 786.7 21.053 0.711

GC661 188.652397438 7.91752050131 933.492 21.532 1.207

GC662 188.280293678 7.61934935504 884.299 20.714 0.454 Continued on next page 96 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC663 188.482583367 7.58861474386 412.24 20.876 0.619

GC664 188.635299619 7.9149196821 893.652 20.924 0.671

GC665 188.463211539 7.67099813759 204.657 20.925 0.682

GC666 188.246885784 7.93499983113 1279.17 21.121 0.923

GC667 188.708514292 7.63088722299 746.77 20.91 0.715

GC668 188.364387311 7.57222465953 702.456 20.88 0.705

GC669 188.253507016 7.62361222163 971.603 20.872 0.705

GC670 188.538161053 7.72748760875 137.428 20.995 0.829

GC671 188.333222743 7.53220683163 882.126 20.652 0.492

GC672 188.831984428 7.81060457474 1217.754 20.794 0.681

GC673 188.43984876 7.70030664825 262.012 21.002 0.899

GC674 188.426738937 7.69470942269 309.579 20.769 0.671

GC675 188.592150444 7.63076696525 377.515 20.866 0.82

GC676 188.6434316 7.42567557451 1091.23 20.754 0.722

GC677 188.327526573 7.53038836483 901.645 20.47 0.455

GC678 188.528879614 7.70107789888 58.959 20.52 0.509

GC679 188.580829611 7.72014185658 256.956 20.55 0.576

GC680 188.756440612 7.42298570968 1326.132 20.75 0.798

GC681 188.787235727 7.80875329357 1064.493 20.615 0.699

GC682 188.750972627 7.48708500834 1148.446 20.909 1.014

GC683 188.445907046 7.70485146814 241.053 20.655 0.788

GC684 188.176944661 7.65653250305 1218.116 20.659 0.804

GC685 188.520571892 7.61784055825 293.989 20.44 0.585

GC686 188.415845576 7.80039537441 504.293 20.361 0.529

GC687 188.61285512 7.53211498513 701.182 20.736 0.905

GC688 188.583658234 7.92709012993 859.628 20.595 0.764

GC689 188.74211559 7.47488725578 1155.072 20.509 0.691 Continued on next page 97

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC690 188.231691029 7.80017722321 1074.794 20.91 1.093

GC691 188.4962268 7.712412 74.638 20.617 0.835

GC692 188.237048598 7.57503377512 1087.987 20.375 0.596

GC693 188.677200751 7.39406037509 1247.834 20.636 0.868

GC694 188.239764909 7.69680108897 982.314 20.477 0.718

GC695 188.448556292 7.92429129668 842.8 20.647 0.889

GC696 188.222740476 7.72995607787 1049.455 20.408 0.651

GC697 188.787849559 7.71807680294 993.175 20.684 0.938

GC698 188.231123641 7.65606519973 1025.17 20.257 0.532

GC699 188.470972022 7.95504837236 933.475 20.484 0.76

GC700 188.521074937 7.42047424793 1003.573 20.359 0.658

GC701 188.764503612 7.87799367396 1112.176 20.454 0.769

GC702 188.614350942 7.4934851271 825.91 20.167 0.487

GC703 188.697137464 7.66927404971 672.894 20.262 0.586

GC704 188.721411081 7.77307422174 797.41 20.162 0.493

GC705 188.176041589 7.53196329123 1352.878 20.258 0.594

GC706 188.408863045 7.94579398814 963.398 20.219 0.566

GC707 188.409537466 7.95891762501 1006.216 20.311 0.661

GC708 188.218392543 7.79154344445 1110.252 20.145 0.524

GC709 188.796732165 7.71360383054 1024.133 20.095 0.496

GC710 188.586304481 7.86914256637 667.102 20.151 0.558

GC711 188.760129063 7.7811770556 938.729 20.107 0.533

GC712 188.409471986 7.72557147319 383.352 20.056 0.486

GC713 188.281092649 7.58130145974 935.205 20.018 0.468

GC714 188.710666101 7.89451414332 1001.568 20.018 0.521

GC715 188.486394968 7.7901816015 341.159 20.564 1.074

GC716 188.62251132 7.91081132443 858.665 19.965 0.487 Continued on next page 98 Appendix A. Appendix A

Table A.1 – continued from previous page ID R.A. Dec. Radius g g-i [degrees] [degrees] [arcsec] [mag] [mag]

GC717 188.620424833 7.43633473174 1022.527 20.239 0.768

GC718 188.399845449 7.79457170186 531.897 20.179 0.77

GC719 188.482752483 7.78507511198 327.6 20.047 0.655

GC720 188.6302622 7.65107149067 457.469 20.032 0.724

GC721 188.778220084 7.80015521686 1023.01 19.868 0.654

GC722 188.31421586 7.52180652286 957.917 20.094 0.889

GC723 188.713527102 7.53727946091 928.736 20.063 0.891

GC724 188.473593904 7.70680753163 143.196 20.187 1.015

GC725 188.46501164 7.80516778216 418.491 19.723 0.62