dynamically-driven galaxy evolution in clusters of galaxies

1. Dynamically-Driven Galaxy Evolution in Clusters of Galaxies Peter Christian Jensen Presented in fullment of the requirements of the degree of Master of Science 2014 Faculty of Science, Engineering and Technology Swinburne University of Technology

2. i Abstract Galaxy evolution is a very active eld of current astrophysical research. Despite this, the question of how cluster mergers modulate the evolution of galaxies is unresolved. Given the ubiquity of cluster mergers and that some 510% of galaxies in the local Universe reside in clusters of galaxies, answering this question is of vital importance for gaining a complete understanding of the processes responsible for galaxy evolution. The aim of this thesis is to study galaxy evolution in a cluster merger and to link galaxy evolution to the merger. We collected optical spectra of galaxies in Abell 3667, a cluster merger system 1 Gyr post-core passage, and in a sample of relaxed benchmark clusters with the 3.9m Anglo-Australian Telescope and 6.5m MMT Telescope down to M + 3. Single stellar population templates were tted to the spectra to measure velocity dispersions and to broadly classify galaxies as absorption line or emission line systems. Lick indices were measured and were used to derive the age, [/Fe], [Fe/H] and [Z/H] stellar population parameters and the Balmer indices were used to classify post-starburst galaxies. Equivalent widths were measured for the H, H, [OIII] and [NII] emission lines. Emission line ratios were used to classify emission line galaxies into star-forming and AGN classes and star formation rates were estimated from H and D4000. In A3667, we found a signicant population of in-falling starburst galaxies associated with the north-west shock front, and a population of post-starburst galaxies distributed along the merger axis between the bilateral north-west and south-east shock fronts. Ap- proximately 80% of the starburst galaxies in the north-west quadrant have positions and specic star formation rates consistent with their star formation having been triggered by shock compression of the surrounding intracluster medium by the north-west shock front. A further 45% of the post-starburst galaxies in the north-west and south-east quadrants have an elongated spatial distribution and cluster-like recession velocities, suggestive that their earlier starburst event coincided with the time of core passage. We also found a population of very old absorption line galaxies in the cluster core, 0.1 dex older than predicted by the massage relation of A3667. In comparison to the relaxed benchmark clusters, A3667 presents as a normal cluster in many of its aggregate properties. This work provides strong evidence that cluster mergers play a signicant and ongoing role in transforming gas-rich galaxies into absorption line galaxies. In A3667, 1.6% of the cluster members are experiencing starbursts, directly-related to shock front interactions, while up to 5% of the cluster members could have experienced an earlier round of starbursts, triggered at the time of core passage.

4. iii Acknowledgements I would like to begin by thanking my principal supervisor, Warrick Couch, and my co- supervisors, Matt Owers, Greg Poole and Paul Nulsen. Without their help, guidance and valuable input, this thesis would not be the masterpiece that it is. I appreciate Warricks candour and experience in guiding me through my research project. We may not have always seen eye-to-eye; however, this work would not have been possible without you as my primary supervisor. Thanks must go to Matt Owers for giving me access to his data and catalogues on Abell 3667, and for help and guidance with processing and analysing the existing data and new data I collected along the way. Thank-you to Greg Poole for enlightening discussions and helping me to get my head around the literature. A special thanks to Paul Nulsen for helping me get telescope time on the MMT, the data from which forms an integral part of this thesis. In addition to my supervisory team, I would also like to mention my examiners, Matthew Colless and Alastair Edge, for spending the time looking over my thesis with a ne-tooth comb. Thanks to all of you for comments and suggestions in editing this thesis; it is a much better piece of work owing to your contributions. A big thank-you to the Australian Astronomical Observatory, without whose telescope time and friendly support sta this thesis would not be possible. I also want to thank Max Spolaor, Rob Proctor and Trevor Mendel for giving me access to their stellar population parameter code, tutorials on how to use and modify the code and helpful discussions on how to interpret the results. Thanks must also go to Jacopo Fritz for re-calculating spectroscopic classication frequencies from the WINGS cluster survey with my desired cuts in magnitude and radial extent, Alexis Finoguenov for providing me with XMM-Newton and SUMMS 843 MHz images of Abell 3667, and Russell Smith for useful discussions re- garding Lick indices and stellar population parameters in the NOAO Fundamental Plane Survey. I also gratefully acknowledge the nancial support of the Australian Government in providing me with an APA scholarship from 20092012. A special word of thanks to all of my support sta at Swinburne University of Tech- nology. In particular, I want to thank Alister Graham and Sarah Maddison for their help in guiding me through the nal stages of my thesis and in helping me to organise my scarce time at the end. Without your help, I would surely have fallen through the cracks on my way to thesis submission. I also want to thank my student councillor, Josh Sasai, for listening to my concerns and crazy talk during some of my darkest moments and helping me to see the light at the end of the tunnel. I dont know how you do your job,

5. iv listening to people like me all day long. You are a true saint! I also want to thank Chris Blake, Virginia Kilborn and Emma Ryan-Weber for taking a special interest in me when I was at my lowest point. It may not seem like a lot to you, but your genuine concern was noted and appreciated. A big shout out to all my friends and associates at the Centre for Astrophysics and Supercomputing, past and present. I especially want to name Gonzalo Diaz, Vincenzo Pota, Juan Madrid, Christina and Michael Smith, Giulia Savorgnan, Adrian Malec, Anna Sippel, Carlos Contreras, Evelyn Caris, Stefan Oslowski, Pierluigi Cerulo, Chris Usher, Glenn Kacprzak, George Hau, Paolo Bonni, Lee Spitler and Rob Crain. You have all been good friends to me, helped me somewhere along the way and shared many good times together. I will remember you all forever, and hopefully this is not the last time we see each other. Apologies to anyone I missed, you know who you are! Finally, I want to thank my family Mum, Dad and my brothers Nicholas and Alexan- der and my ex-partner, Soe Ham. Without your love and support I surely would have folded many years ago. Im sorry if I have driven you to despair with the stress of com- pleting a thesis. I know that sometimes the others around me feel the pressure even worse than I do. Thank-you for your love and for believing in me.

7. vi Declaration The work presented in this thesis has been carried out in the Centre for Astrophysics & Supercomputing at Swinburne University of Technology between 2009 and 2014. 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. Peter Christian Jensen Melbourne, Victoria, Australia September 18, 2014

8. vii In loving memory of Tanya Ham (19512011) The cosmos is within us. We are made of star stu . . . Carl Sagan

9. Contents Abstract i Acknowledgements ii Declaration v List of Figures xi List of Tables xiv 1 Introduction 1 1.1 Galaxy Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Cluster Mergers and Their Role in Galaxy Evolution . . . . . . . . . . . . . 3 1.2.1 Cluster Mergers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Cold Fronts As Signposts of Cluster Mergers . . . . . . . . . . . . . 4 1.2.3 The Link Between Cluster Mergers and Galaxy Evolution . . . . . . 6 1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Observations 13 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Cluster Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.1 Abell 3667 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2 Relaxed Benchmark Cluster Sample . . . . . . . . . . . . . . . . . . 14 2.3 Observing Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 AAT Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4.1 Target Selection and Prioritisation . . . . . . . . . . . . . . . . . . . 17 2.4.2 Telescope and Instrumentation . . . . . . . . . . . . . . . . . . . . . 22 2.4.3 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.4 Redshift Measurements . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.4.5 Spectroscopic Completeness . . . . . . . . . . . . . . . . . . . . . . . 32 2.5 MMT Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5.1 Target Selection and Prioritisation . . . . . . . . . . . . . . . . . . . 32 2.5.2 Telescope and Instrumentation . . . . . . . . . . . . . . . . . . . . . 36 2.5.3 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.5.4 Redshift Measurements . . . . . . . . . . . . . . . . . . . . . . . . . 37 ix

10. x Contents 2.5.5 Spectroscopic Completeness . . . . . . . . . . . . . . . . . . . . . . . 40 2.6 Cluster Membership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.6.1 Abell 3667 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.6.2 Relaxed Benchmark Clusters . . . . . . . . . . . . . . . . . . . . . . 42 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 Spectroscopic Measurements 49 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.2 Spectral Template Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.2.1 Pre-processing Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.2 Template Fitting Algorithm . . . . . . . . . . . . . . . . . . . . . . . 53 3.3 Absorption Line Measurements . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.1 Velocity Dispersion Measurements . . . . . . . . . . . . . . . . . . . 55 3.3.2 Lick Index Measurements . . . . . . . . . . . . . . . . . . . . . . . . 62 3.3.3 Absorption Line Galaxy Classications . . . . . . . . . . . . . . . . . 67 3.3.4 Stellar Populations Measurements . . . . . . . . . . . . . . . . . . . 79 3.4 Emission Line Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.4.1 Equivalent Width Measurements . . . . . . . . . . . . . . . . . . . . 82 3.4.2 Emission Line Galaxy Classications . . . . . . . . . . . . . . . . . . 92 3.4.3 Star Formation Rate Measurements . . . . . . . . . . . . . . . . . . 95 3.5 Final Galaxy Catalogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4 Abell 3667 Galaxy Evolution in a Major Cluster Merger 115 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4.2 Spectroscopic Classications . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 4.2.1 Global Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 4.2.2 Local Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 4.3 Star Forming Galaxies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 4.3.1 Global Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 4.3.2 Local Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 4.4 Stellar Population Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 145 4.4.1 Global Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 4.4.2 Local Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

11. Contents xi 5 Concluding Remarks 161 5.1 Work Done . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 5.3 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Bibliography 177 A Galaxy Catalogue 179

12. List of Figures 2.1 SDSS r-band images of the cores of Abell 963 and Abell 1650 and Super- COSMOS rF -band image of the core of Abell 3827 with Chandra X-ray brightness contours overplotted . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Colour-magnitude diagrams of the parent photometric catalogues for Abell 1650 and Abell 3827 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 Abell 1650 recession velocity comparison and recession velocity residuals . . 29 2.4 Abell 3667 recession velocity comparison and recession velocity residuals . . 30 2.5 Abell 3827 recession velocity comparison and recession velocity residuals . . 31 2.6 Abell 1650 spectroscopic completeness as a function of projected distance and R-band magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.7 Abell 3827 spectroscopic completeness as a function of projected distance and rF -band magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.8 Colour-magnitude diagram of the parent photometric catalogue for Abell 963 36 2.9 Abell 963 recession velocity comparison and recession velocity residuals . . 39 2.10 Abell 963 spectroscopic completeness as a function of projected distance and R-band magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.11 Radial velocity dispersion prole and cluster membership diagram for Abell 963 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.12 Radial velocity dispersion prole and cluster membership diagram for Abell 1650 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.13 Radial velocity dispersion prole and cluster membership diagram for Abell 3827 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1 Representative emission and absorption line spectra with their best tting model templates overplotted . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2 Abell 963 velocity dispersion comparison . . . . . . . . . . . . . . . . . . . . 57 3.3 Abell 1650 velocity dispersion comparison . . . . . . . . . . . . . . . . . . . 59 3.4 Abell 3667 velocity dispersion comparison . . . . . . . . . . . . . . . . . . . 61 3.5 Abell 3827 velocity dispersion comparison . . . . . . . . . . . . . . . . . . . 63 3.6 Abell 963 Lick index comparisons . . . . . . . . . . . . . . . . . . . . . . . . 68 3.7 Abell 1650 Lick index comparisons . . . . . . . . . . . . . . . . . . . . . . . 70 3.8 Abell 3667 Lick index comparisons . . . . . . . . . . . . . . . . . . . . . . . 72 3.9 Abell 3827 Lick index comparisons . . . . . . . . . . . . . . . . . . . . . . . 74 xiii

13. xiv List of Figures 3.10 A comparison of repeat measurements of the eective equivalent width of H in our sample of galaxies . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 3.11 Stellar population parameter repeat measurements comparisons . . . . . . . 81 3.12 Abell 963 emission line equivalent width comparisons . . . . . . . . . . . . . 87 3.13 Abell 1650 emission line equivalent width comparisons . . . . . . . . . . . . 89 3.14 Abell 3667 emission line equivalent width comparisons . . . . . . . . . . . . 90 3.15 Abell 3827 emission line equivalent width comparisons . . . . . . . . . . . . 91 3.16 BPT and Cid Fernandes et al. (2010) emission line galaxy classication diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.17 Representative emission and absorption line galaxy spectra for each of the major spectroscopic classes described in the text . . . . . . . . . . . . . . . 96 3.18 The SFR/MD4000 relation and comparison of repeat D4000 measurements for all star-forming galaxies in our data set . . . . . . . . . . . . . . . 100 3.19 Star formation rate, specic star formation rate and Scalo birthrate parameter comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.1 Spectroscopic classication maps for Abell 3667 . . . . . . . . . . . . . . . . 131 4.2 Spectroscopic classication phase space diagrams for Abell 3667 . . . . . . . 132 4.3 Star formation rate and specic star formation rate maps for Abell 3667 . . 144 4.4 Stellar population parameter scaling relations for Abell 3667 . . . . . . . . . 147 4.5 The luminosity-weighted stellar age map for Abell 3667 . . . . . . . . . . . 152 4.6 The luminosity-weighted /Fe map and /Fe residuals map for Abell 3667 153 4.7 The luminosity-weighted Z/H map and Z/H residuals map for Abell 3667 . 154 4.8 The luminosity-weighted Fe/H map and Fe/H residuals map for Abell 3667 155

14. List of Tables 2.1 Observing program summary . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Summary of key cluster observational properties . . . . . . . . . . . . . . . 17 2.3 Analysis of AAT recession velocity repeat measurements . . . . . . . . . . . 28 3.1 Analysis of velocity dispersion repeat measurements . . . . . . . . . . . . . 56 3.2 Analysis of Lick index repeat measurements for Abell 963 . . . . . . . . . . 69 3.3 Analysis of Lick index repeat measurements for Abell 1650 . . . . . . . . . 71 3.4 Analysis of Lick index repeat measurements for Abell 3667 . . . . . . . . . 73 3.5 Analysis of Lick index repeat measurements for Abell 3827 . . . . . . . . . 75 3.6 Analysis of emission line equivalent width repeat measurements . . . . . . . 86 3.7 Sample table of characteristic galaxy properties including cluster membership and astrometric, photometric, kinematic and spectroscopic signal-to- noise ratio measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 3.8 Sample table of D4000 and He index measurements and Lick index measurements from HA to HA . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 3.9 Sample table of Lick index measurements from HF to Mg2 . . . . . . . . . 110 3.10 Sample table of Lick index measurements from Mg b to TiO2 . . . . . . . . 111 3.11 Sample table of emission line equivalent width and amplitude-to-noise ratio measurements and emission line galaxy types . . . . . . . . . . . . . . . . . 112 3.12 Sample table of stellar population parameter measurements, star formation rate measurements and spectroscopic classications . . . . . . . . . . . . . . 113 4.1 Galaxy counts and frequencies for all cluster members in Abell 3667 down to rF = 19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 4.2 Galaxy counts and frequencies for Abell 3667 and the Relaxed Benchmark Cluster Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 4.3 Comparison between Abell 3667 and WINGS galaxy counts and spectroscopic classication frequencies within 1.32 r200 . . . . . . . . . . . . . . . . 125 4.4 Comparison between Abell 3667 and LARCS galaxy counts and spectroscopic classication frequencies down to M + 1.5 and within r200 . . . . 127 4.5 Galaxy counts and frequencies, sorted by region, for all cluster members in Abell 3667 down to rF = 19 . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 4.6 Total cluster star formation rates, mean cluster star formation rates, and specic cluster star formation rates for Abell 3667 and the Relaxed Bench- mark Cluster Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 xv

15. xvi List of Tables 4.7 Total substructure star formation rates and mean substructure star formation rates for Abell 3667 and its substructures down to rF = 19 . . . . . . . 145 4.8 Comparison between our stellar population parameter scaling relations for Abell 3667 and stellar population parameter scaling relations of various galaxy cluster surveys at a similar redshift to A3667 in the literature. . . . 148 A.1 Characteristic galaxy properties including cluster membership and astrometric, photometric, kinematic and spectroscopic signal-to-noise ratio measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 A.2 Catalogue of D4000 and He index measurements and Lick index measurements from HA to HA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 A.3 Catalogue of Lick index measurements from HF to Mg2 . . . . . . . . . . . 402 A.4 Catalogue of Lick index measurements from Mg b to TiO2 . . . . . . . . . . 511 A.5 Catalogue of emission line equivalent width and amplitude-to-noise ratio measurements and emission line galaxy types . . . . . . . . . . . . . . . . . 620 A.6 Catalogue of stellar population parameter measurements, star formation rate measurements and spectroscopic classications . . . . . . . . . . . . . . 729

16. 1Introduction 1.1 Galaxy Evolution Galaxy evolution is a very active eld of current astrophysical research. Some of the more signicant lines of observational evidence demonstrating the phenomenon of galaxy evolution include: (i) the increasing fraction of early type (elliptical and lenticular) galaxies and complementary decline in the fraction of late type (spiral and irregular) galaxies with galaxy number density (the morphology-density relation; Dressler, 1980) which suggests that late type galaxies are transformed into early type galaxies in dense environments; (ii) the decreasing fraction of early type galaxies and complementary increase in the fraction of late type galaxies in clusters as a function of redshift (Poggianti et al., 2009) which suggests that the morphology-density relationship itself has evolved with time and that galaxy evolution might be more ecient in intermediate-density environments (e.g., low mass clusters, groups of galaxies and laments) than in high mass clusters (pre- processing; Wilman et al., 2009); (iii) the increasing fraction of blue cluster galaxies with redshift (the Butcher-Oemler eect; Butcher & Oemler, 1978, 1984) demonstrates that clusters of galaxies have steadily built up their red galaxy population while the fraction of star-forming galaxies has declined rapidly since at least z 0.4; (iv) the decreasing star formation rate (SFR) density of the Universe (Madau et al., 1996; Steidel et al., 1999; Hopkins, 2004) is clear evidence for the fact that the SFR of galaxies has not been universally constant through cosmic time and has been in rapid decline since z 3; more recently, (Haines et al., 2013) showed that the decline proceeds even more rapidly in clusters of galaxies than in the eld, highlighting the impact of environmental processes on the decreasing star formation rate densities; 1

17. 2 Chapter 1. Introduction (v) the declining galaxy star formation rate with decreasing clustercentric distance (Lewis et al., 2002) and with increasing galaxy number density (Gomez et al., 2003) provides evidence for a SFR-density relation; (vi) the very existence of post-starburst (E+A) galaxies implies abrupt truncation of galaxy SFR rates over timescales much shorter than the Hubble time (Couch & Sharples, 1987); (vii) the decreasing mass distribution of E+A galaxies in clusters with decreasing redshift (E+A downsizing; Tran et al., 2003; Poggianti et al., 2004) implies that starbursts have occurred in increasingly less massive galaxies up to the current epoch; and (viii) the increasingly red colours of quiescent galaxies with increasing luminosity (the colour-magnitude relation; Visvanathan & Sandage, 1977) has been interpreted as a mass- metallicity eect (Tremonti et al., 2004) whereby massive quiescent galaxies hold onto more of their metals due to their deeper gravitational potentials than their low-mass counterparts, thus they appear redder in colour. It has even been shown that the slope of the colour-magnitude relation is itself subject to redshift evolution shown (Stott et al., 2009). All of this observational evidence, of which we only provide a brief overview, illustrates the fact that galaxies not only evolve in time (redshift), but that galaxy evolution is also driven by environmental and secular processes. Furthermore, there is no shortage of candidate physical mechanisms that have been proposed to drive galaxy evolution. A few examples appear to be serious contenders: e.g., ram pressure stripping (Gunn & Gott, 1972; Bekki & Couch, 2003), viscous stripping (Nulsen, 1982), galaxy harrassment (Moore et al., 1996), strangulation (Larson et al., 1980), tidal interaction with the cluster potential (Bekki, 1999), and galaxygalaxy interactions (Lavery & Henry, 1988), although it is unclear which of these are important. Whilst new research is constantly being produced demonstrating evidence for galaxy evolution (e.g., Price et al., 2011 and Smith et al., 2012 who examine the evolution of stellar populations of cluster galaxies), the focus of current research has been to determine which mechanism is pre-eminent in galaxy evolution. To date, however, there still does not appear to be any coherent scientic consensus about which one is most important. Even less certain is the role (if any) that hierarchical structure formation and cluster mergers play in modulating these mechanisms.

18. 1.2. Cluster Mergers and Their Role in Galaxy Evolution 3 1.2 Cluster Mergers and Their Role in Galaxy Evolution 1.2.1 Cluster Mergers Clusters of galaxies are the largest, most massive, virialised objects in the Universe. In the hierarchical growth scenario, clusters grow through the accretion of lower-mass subclusters (minor mergers), and occasionally via mergers with similar mass clusters (major mergers). Such major mergers are the most energetic phenomena in the universe since the Big Bang, colliding at 2000 km s1 and releasing some 1064 erg of gravitational binding energy. Some 10% of this energy is injected into the baryonic components via shock heating, adiabatic compression of the intracluster medium (ICM), acceleration of particles to cosmic ray energies, and imparted peculiar motions in the galaxies (Markevitch et al., 1999; Sarazin, 2002). The observational signatures of cluster mergers are many and span the complete elec- tromagnetic spectrum. In the optical and NIR, the main strategy is to look for substructures of galaxies within clusters of galaxies. This can be achieved by performing 1D galaxy overdensity tests along the line-of-sight using the cluster members spectroscopic redshift measurements (e.g., the KolmogorovSmirnov KS test; Press et al., 1992, the GaussHermite test; Zabludo et al., 1993), 2D galaxy overdensity tests in the plane of the sky using the cluster members astrometry measurements (e.g., the angular separation and symmetry tests; West et al., 1988, the Lee statistic; Fitchett & Webster, 1987), and 3D galaxy overdensity tests combining the cluster members astrometric and redshift measurements (e.g., the k-statistic; Colless & Dunn (1996), the delta test; Dressler & Shectman, 1988). In these tests, signicant departures from the distributions expected for spherically-symmetric, relaxed, dynamic systems are considered to be evidence for the existence of substructure (see Pinkney et al., 1996 for an overview of substructure detection tests). Weak gravitational lensing measurements have also been successfully used to detect substructures in the projected mass maps of Abell 3667 (Jore et al., 2000) and the Bullet Cluster (1ES0657-558; Markevitch et al., 2004; Clowe et al., 2006). The latter example is of particular interest, as it shows decoupling of the dark matter 1 and dominant baryonic (i.e. the ICM) matter components in the plane of the sky, providing the most convincing evidence to date for the existence of dark matter in clusters of galaxies. At X-ray and radio wavelengths, the main strategy is to look for hydrodynamic signatures of cluster mergers in the ICM. At X-ray wavelengths, elongated, non-spherical or morphologically-disturbed X-ray surface brightness maps are indicative of a major merger 1 the galaxies in the Bullet Cluster are roughly spatially coincident with the lensing mass peaks

19. 4 Chapter 1. Introduction (Knopp et al., 1996). Multiply-peaked X-ray surface brightness maps betray the existence of multiple cluster cores; this is all the more convincing when the X-ray peaks are coincident with galaxy overdensity peaks or close to the positions of D and cD galaxies (Knopp et al., 1996). X-ray surface brightness edges due to shock fronts and cold fronts suggest the movement of large, stable bodies of gas within the ICM (Forman et al., 2002; Sarazin, 2002), also indicative of a major cluster merger. Indeed, with the increased spatial resolution and sensitivity of the latest generation of satellite-borne X-ray telescopes (e.g., Chandra X-ray Observatory; XMM-Newton), surface brightness edges are fast becoming one of the most reliable signposts of cluster mergers (Owers, 2008; 2009c; 2009a; 2009b; 2011b; 2011a). At radio wavelengths, diuse, megaparsec-scale, low-surface brightness radio sources with steep spectral indices are found in a few massive, irregular clusters, all of which appear to be undergoing cluster mergers (Lacy et al., 1993; Feretti & Giovannini, 2008). Roughly symmetric sources that are projected onto the cluster core are known as radio haloes (e.g., the Coma cluster; Deiss et al., 1997) whereas sources that are projected onto the cluster periphery are known as radio relics (e.g., Abell 3667; Rottgering et al., 1997). Given that such radio sources are only found in cluster mergers, it is suggestive that the radio emitting electrons are accelerated primarily by merger shocks or turbulence in the wake of the cluster merger (Feretti & Giovannini, 2008). More recently, ZuHone et al. (2013) demonstrated that mini-haloes can be produced by merger-induced sloshing of cool core gas. Bliton et al. (1998) also state that narrow-angle tailed (NAT) radio galaxies are preferentially located in clusters undergoing a cluster merger. They suggest that merger-induced bulk ows in the ICM may be partly responsible for the U-shaped bending of the NAT galaxies radio jets. 1.2.2 Cold Fronts As Signposts of Cluster Mergers Enquiry into the nature and behaviour of the ICM has recently been enabled at un- precedented levels of spatial resolution and sensitivity by the latest generation of X-ray telescopes. One of the earliest results to come from Chandra was the discovery of the true nature of the extended X-ray surface brightness edges in Abell 2142 (Markevitch et al., 2000) and Abell 3667 (Vikhlinin et al., 2001). The X-ray edges in A3667 were originally interpreted as shock fronts using lower resolution ROSAT and ASCA data (Markevitch et al., 1999), however, subsequent analysis of the new Chandra data by Vikhlinin et al. (2001) reinterpreted the edges as being cold fronts. A cold front is a contact discontinu- ity between cold, dense gas embedded in a hot, diuse ICM. Cold fronts are distinguishable

20. 1.2. Cluster Mergers and Their Role in Galaxy Evolution 5 from shock fronts in that the cooler gas is found on the brighter (higher-density) side of the edge while the gas pressure prole is continuous across the edge. Markevitch et al. (2000) and Vikhlinin et al. (2001) interpreted the cold fronts in Abell 2142 and Abell 3667 as being the remnant cool cores of the merging subclusters, analogous to the archetypal remnant cool core in the Bullet Cluster (Owers et al., 2009b). Subsequent high-resolution observations of seemingly X-ray relaxed-looking clusters, (e.g., Abell 1795; Markevitch et al., 2001, Abell 2029; Clarke et al., 2004, and RXJ1720.1+2638; Mazzotta et al., 2001), revealed the existence of a more subtle class of cold front, the so-called sloshing type cold front. In an analogy to the sloshing of wine out of a glass, it is thought that sloshing type cold fronts are produced when relative motion is induced between the cool core gas at the bottom of a clusters gravitational potential well and the hotter surrounding ICM by a perturbative force. In this scenario, cold fronts are formed at the interface between the sloshed-out, cool core gas and hotter ICM at larger radius. Simulations have been able to reproduce sloshing type cold fronts with perturbations induced by in-falling subclusters and dark matter haloes (Ascasibar & Markevitch, 2006; Poole et al., 2006; Roediger et al., 2011). Other authors suggest that weak shocks (Churazov et al., 2003) and acoustic waves (Fujita et al., 2004) may also be candidate perturbers. Current authors (e.g., Owers et al., 2009b; Ascasibar & Markevitch, 2006) suggest that dierences between remnant cool core cold fronts and sloshing type cold fronts reect dierences in the scale of the cluster merger rather than dierent mechanisms of formation. In this scenario, remnant cool core cold fronts are formed by major cluster mergers whereas relaxed-looking or sloshing type cold fronts are formed by minor cluster mergers. There is little doubt that cold fronts are excellent signposts of recent post-core passage cluster mergers. By application of quantitative 3-D substructure tests, Owers et al. (2009c; 2009a; 2011b; 2011a) have demonstrated a clear relationship between the existence of prominent cold fronts and signicant substructure tied to recent post-core passage merger scenarios in Abell 1201, Abell 2142, Abell 2744, Abell 3667, and RXJ1720.1+2638. Other X-ray observations have shown that the majority of cool core clusters exhibit some form of cold front (Ghizzardi et al., 2010). Furthermore, simulations indicate that some 4050% of the mass and number of galaxies in massive clusters at the current epoch have been agglomerated via minor and major mergers (Berrier et al., 2009; McGee et al., 2009). Thus it appears that cluster mergers are relatively ubiquitous phenomena in the local Universe and that cold fronts are reliable observational signposts of recent post-core passage cluster mergers oering signicant advantages over other detection methods.

21. 6 Chapter 1. Introduction 1.2.3 The Link Between Cluster Mergers and Galaxy Evolution As discussed in the previous section, there is much observational evidence for galaxy evolution. Many candidate physical mechanisms have been proposed for driving cluster galaxy evolution, although it is unclear which of them are important. Even less clear is the role hierarchical structure formation plays in enhancing and/or modulating these physical mechanisms and whether the most extreme events of hierarchical growth, i.e. cluster mergers, can provide a catalyst for galaxy transformation processes. Why might we think that mergers of clusters of galaxies could play a role in galaxy transformation processes? Major cluster mergers result in dramatic recongurations of clusters of galaxies kinetic energy is imparted into the peculiar motions of cluster members, the ICM is disturbed via adiabatic compression and shock heating processes while the dark matter halo increases in mass resulting in a deeper gravitational potential well. These processes ensure that at least some of the cluster members experience rapidly chang- ing local environments over typical timescales of a few Gyr. It is certainly plausible that increased velocity dispersions due to the merger and larger halo mass could increase the levels of galaxygalaxy harassment (Moore et al., 1996) and galaxycluster tidal forces (Bekki, 1999) experienced by gas-rich cluster member galaxies, tidally stripping them of their stars and star-forming gas, inducing transformations from early to late type morphologies while also augmenting the intracluster stellar population. Interactions with shocks could rapidly increase the external pressure of the ICM by an order of magnitude (Bekki et al., 2010), triggering bursts of star formation (e.g., jellysh galaxies; Owers et al., 2012), modulating the fractions of star-forming and quiescent galaxies, possibly via starburst and post-starburst phases, while the heating of or turbulence induced in the ICM could increase the rate of thermal gas evaporation (Cowie & Songaila, 1977) experienced by those galaxies or slowly strangulate them of their halo gas reservoirs (Bekki et al., 2002). Furthermore, dynamically-driven, hydrodynamic instabilities inside the cluster members could potentially shepherd material into their inner regions resulting in ephemeral bursts of AGN activity (Miller & Owen, 2003) and, speculatively, central starbursts (Combes, 2001). A number of observational and theoretical studies have been undertaken to investigate the link between galaxy evolution and cluster dynamical growth. Caldwell et al. (1993) and Caldwell & Rose (1997) are arguably the rst authors to present systematic, observational evidence for cluster merger-driven galaxy evolution in a sample of ve nearby Butcher- Oemler clusters, three of which were purposefully selected on the basis that they showed evidence of a cluster merger. The primary conclusion of the paper was that 15% of

22. 1.2. Cluster Mergers and Their Role in Galaxy Evolution 7 early type galaxies in their sample are abnormal, showing evidence of ongoing or recent star formation, however, at a reduced frequency and burst strength compared to more distant Butcher-Oemler clusters. Based on their kinematic studies, the authors argue that some of their clusters are in a 1 Gyr post-core passage merger phase, consistent with the Gyr post-starburst timescale of their abnormal galaxies, speculating that the starbursts are triggered by shocks in the ICM during or after core passage. Comparative observations by Hwang & Lee (2009) of Abell 168 and Abell 1750 suggested that Caldwell et al.s hypothesis may be correct in so far that galaxy evolution is unlikely to be observed prior to core passage in a cluster merger. Whereas Abell 168 is in an advanced, post- core passage merger state and has enhanced star formation or AGN activity between its subcluster components, Abell 1750 is in an early pre-core passage merger state and shows no evidence of enhanced star formation or AGN activity between its subcluster components. Caldwell et al.s hypothesis was very recently shown to be plausible by Stroe et al. (2014) who presented evidence that the normalisation of the H luminosity function is boosted by an order of magnitude in the radio relic area of the Sausage Cluster (CIZA J2242.8+5301). Bekki et al. (2010) also numerically demonstrated that the external pressure of the ICM can be increased to levels sucient to trigger ecient star formation in gas-rich cluster members during a major merger. This prediction, however, is at odds with Fujita et al. (1999) whose simulations show that the external pressure of the ICM due to cluster mergers is more likely to ram pressure strip the gas rich cluster members without triggering any signicant starburst events. Furthermore, Bekki et al. (2010) predict that the transformed post-starburst galaxies should have a weakly-elongated spatial distribution in the direction of the cluster merger, dierent from the rest of the cluster galaxy population, however, Poggianti et al. (2004) observed that the post-starburst population of the Coma Cluster, suggested by many authors (e.g., Briel et al., 1992; Biviano et al., 1996; Buote, 2002; Colless & Dunn, 1996; Smith et al., 2012) as being in a post-core passage merger state, does not show any preferential location within the cluster. Miller & Owen (2003) performed a multi-wavelength observational study of the major cluster merger in Abell 2255. Benchmarking their results against the 19 other nearby Abell clusters in Miller & Owen (2002), the authors presented strong evidence for an increased frequency of radio galaxies in Abell 2255. The radio galaxies in this cluster were associated with powerful radio AGNs and optically-faint, star-forming galaxies, the latter class having optical spectra generally consistent with recent or ongoing starbursts. They also found that their optically faint star-forming galaxies were distributed along

23. 8 Chapter 1. Introduction an axis perpendicular to the probable merger axis. Assuming that these galaxies are in fact the progenitors of a merger-driven, post-starburst galaxy population, the observed distribution is roughly orthogonal to and inconsistent with the distribution claimed by Bekki et al. (2010). Mergers of clusters out to z 0.6 appear to have similar eects on their cluster members as for the nearby Abell clusters discussed above. For example, Ma et al. (2010) report that all of their observed post-starburst galaxies in MACS J0025.4-1225 (z = 0.586) are located close to the X-ray center of the cluster, midway between the dark matter peaks which had a core passage some 0.51 Gyr ago. This is in stark contrast to other intermediate-redshift clusters whose post-starburst galaxies preferentially reside in lower density environments (e.g., Dressler et al., 1999; Tran et al., 2003). Interestingly, Ma et al. also report that 70% of galaxies in the center of the cluster also have lenticular (S0) morphologies which is among the highest to date for a cluster at z > 0.5. Thus it seems that major mergers of galaxy clusters are spatially-associated with post-starburst galaxies as well as the morphological change of cluster members into late types in the local Universe through to the intermediate-redshift Universe. Merger-driven galaxy evolution can also be studied by looking at the stellar populations of the galaxies. Smith et al. (2012) performed an analysis of the stellar populations of the galaxies within the Coma cluster and its ongoing merger with the NGC 4839 group. They reported that the ages of the red sequence dwarf galaxies were primarily correlated with clustercentric distance whereas the ages of the red sequence giant galaxies were primarily correlated with galaxy mass. This indicates that the cluster merger environment can have a signicant impact on the ages of the less massive cluster members. While post-starburst galaxies have undoubtedly experienced an earlier starburst phase, some of them presumably merger-induced around the time of core passage, diculties in identifying cluster mergers in the process of core crossing, disentangling the merger- induced starbursts from in-fall starbursts, and the relatively short ( 100 Myr) starburst timescale in comparison to the longer ( Gyr) timescale of the rst core passage means that it is challenging to nd smoking-gun evidence that directly links cluster merger activity to the production of starburst galaxies and subsequent post-starburst galaxies. Even well-segregated, pre- and post-core passage mergers are dicult to detect optically. For example, Caldwell & Rose (1997) selected merger candidates based on late Bautz-Morgan morphologies (Bautz & Morgan, 1970) followed up by laborious spatial and kinematical analyses of the cluster. For this reason, the cold front detection method oers a much more ecient and reliable means of identifying recent post-core passage cluster mergers. Fur-

24. 1.3. Motivation 9 thermore, deep spectroscopic surveys are needed to conrm cluster mergers (e.g., Owers et al., 2009a detected substructure in Abell 3667 when probing down to M + 3, whereas Johnston-Hollitt et al., 2008 did not in their shallower sample) and also to identify the faint post-starburst galaxy population of nearby clusters (Poggianti et al., 2004). 1.3 Motivation The question of how cluster mergers modulate the evolution of galaxies is unresolved. Given the ubiquity of cluster mergers and also that some 510% of galaxies in the local Universe reside in clusters of galaxies (Bahcall, 1977), answering this question is of vital importance in gaining a more complete understanding of the processes responsible for galaxy evolution. To date, our knowledge of the eects of cluster mergers on galaxy evolution has been limited by diculties in identifying recent cluster mergers, gauging the scale of cluster mergers, correlating galaxies with substructure and nding suitable, relaxed benchmark clusters against which to compare results. To address the issue, we have selected a well-studied, archetypal merging cluster system, Abell 3667, to explore the relationship between a post-core passage, major cluster merger and galaxy evolution. Observationally, Abell 3667 is a nearby (z = 0.0553), massive ( = 1056 km s1; Owers et al., 2009a, LX = 5.1 1044 erg s1; Ebeling et al., 1996) rich cluster (n = 550) which appears to be undergoing a major merger in the plane of the sky (Owers et al., 2009a). Evidence for an ongoing, post-core passage, major merger in Abell 3667 can be found in its prominent cold front (Markevitch et al., 1999; Vikhlinin et al., 2001); its elongated, disturbed, double-peaked X-ray morphology coincident with two D galaxies (Knopp et al., 1996); detection of approximately equal-mass kinematic substructures within the cluster (Owers et al., 2009a); detection of substructure in the isopleths (Proust et al., 1988; Sodre et al., 1992); twin, steep-spectrum radio relics orien- tated perpendicular to the axis of elongation (Rottgering et al., 1997) with the north-west radio relic being associated with a shock front in the ICM (Finoguenov et al., 2010); detection of a NAT radio galaxy (Rottgering et al., 1997); the multimodal, weak gravitational lensing maps of Jore et al. (2000); and its Bautz-Morgan intermediate type I-II optical morphology Abell et al. (1989). Building upon the previous work of Owers et al. (2009a), we are the rst authors to systematically study the eects of an indisputable cluster merger on the galaxy evolution properties of a sample of galaxies in Abell 3667 with well-dened substructures, complete down to M + 3. The purpose of this thesis is to demonstrate how the ongoing cluster merger in Abell 3667 has modulated the star-formation proper-

25. 10 Chapter 1. Introduction ties, post-starburst galaxy population, luminosity-weighted ages and stellar populations of the galaxies, interpreting the results in terms of their spatial distribution with respect to the kinematic substructures detected by Owers et al. (2009a) and other pertinent merger features. This thesis will demonstrate that we have overcome the key diculties associated with identifying recent cluster mergers and correlating galaxy evolution results with substructure in the cluster, Abell 3667. More work still needs to be done on gauging the scale of cluster mergers and in providing suitable benchmarks against which to compare our results. To address the latter issue, we have targeted three additional clusters that we consider to be dynamically-relaxed Abell 963, Abell 1650 and Abell 3827 on the basis of their lack of any discernible cold front. A rigorous analysis comparing these relaxed benchmark clusters to Abell 3667 is beyond the scope of this thesis, however, we will present our catalogued spectroscopic measurements of the benchmark sample and a preliminary comparative analysis in this work. These auxiliary observations will enable a consistent apples with apples comparison between Abell 3667 and an homogeneous, relaxed benchmark cluster sample. This benchmarking project will be the subject of future work. 1.4 Thesis Outline This thesis is structured as follows: Chapter 2: In this chapter we will discuss how we selected our cluster sample, our observing program, how we obtained and processed our raw spectroscopic data, as well our redshift and spectroscopic completeness measurements, and how we assigned cluster membership to our observations. Chapter 3: In this chapter we discuss the measurements we performed on our processed spectroscopic observations. We discuss, in detail, our spectral template t- ting algorithm, absorption and emission line measurements, our spectroscopic galaxy classication scheme, stellar population parameter measurements, and star formation rate measurements. At the end of the chapter we present a catalogue of our nal spectroscopic measurements. Chapter 4: In this chapter we analyse the galaxy evolution properties of Abell 3667, an archetypal case of a galaxy cluster undergoing a post-core passage, major cluster merger in the plane of the sky. We examine the frequencies and spatial distribution of galaxies by spectral classication, stellar population parameters, and

26. 1.4. Thesis Outline 11 star formation rate measurements. We compare and contrast our results with the literature and interpret our results to make conclusions about whether the cluster merger in A3667 has played a role in the galaxy evolution of its cluster members. Chapter 5: In this chapter we summarise the work done, our major ndings, and discuss the limitations of this study and future work to be done. Throughout this thesis, we assume a standard CDM cosmology (Ade et al., 2013; Bennett et al., 2013) where m = 0.3, = 0.7 and h = H0/(100 km s1 Mpc1 ) = 0.7. Physical distances were calculated using these parameters.

27. 2Observations 2.1 Overview This thesis aims to explore the relationship between a major cluster merger and the optical, spectroscopic properties of a well-studied, dynamically-active cluster and to provide a benchmark sample of dynamically-relaxed clusters for future work. To such an end, four galaxy clusters were selected for this study on the basis of relaxed X-ray morphology and degree of kinematical substructure. Abell 3667 (A3667) was chosen because it is a prime example of a major cluster merger. Three other clusters Abell 963 (A963), Abell 1650 (A1650) and Abell 3827 (A3827) are dynamically-relaxed according to the selection criteria discussed below and form the Relaxed Benchmark Cluster Sample. In this chapter we will explore (i) our cluster selection scheme; (ii) our optical, spectroscopic observing program; (iii) how we obtained and processed our raw Anglo-Australian Telescope and MMT observations, including in-depth discussions about target selection and prioritisation schemes, the telescopes and instrumentation, data reduction techniques, redshift measurements, spectroscopic completeness, and; (iv) our cluster membership analyses. 2.2 Cluster Selection 2.2.1 Abell 3667 Much work has been done studying the kinematic properties of Abell 3667, providing many lines of evidence that this cluster is undergoing a major cluster merger. The evidence has been discussed in detail in Chapter 1 (Section 1.3), hence here we will focus on Owers et al. (2009a; hereafter OCN09a), upon whose work we will build. OCN09a performed a kinematical analysis of Abell 3667 and showed conclusively that it had three main subcomponents based on redshift measurements of 550 spectroscopically- 13

28. 14 Chapter 2. Observations conrmed cluster members. The key conclusions of this paper were that A3667 has signif- icant kinematic substructure and that the inferred merger scenario can be directly linked to key features in the X-ray morphology of the cluster. Using Kayes Mixture Modelling (KMM) algorithm of Ashman et al. (1994), OCN09a identied within the cluster a primary component coincident with the Brightest Cluster Galaxy (BCG), a subcluster component coincident with the second BCG and a smaller group which are respectively referred to as KMM5, KMM2 and KMM4 in their terminology. OCN09a suggest that the most likely merger scenario is one in which the main cluster (KMM5) and the subcluster (KMM2) are undergoing a roughly 3:1 mass merger in roughly the plane of the sky. The subcluster appears to be travelling in a north-westerly direction, having passed through the core of the main cluster approximately 1 Gyr ago. They suggested the cold front, south-east of the cluster core, was formed when a ume of cold gas was sloshed out of the main cluster after core passage. The interpretation of the south-easterly group (KMM4) is that it is either an unbound foreground/background object or that it was stripped from the subcluster during core passage. OCN09a also suggest an alternative scenario in which A3667 is undergoing a 3-body merger. The relationship between the main cluster and the subcluster is the same as in the previous scenario, however, the group has also undergone core passage, proceeding in a south-easterly direction. In this scenario, the cold front is the remnant cool core of the KMM4 group. This paper and a series of other papers (Owers et al., 2009b; 2009c; 2011a; 2011b) have built up a large body of evidence suggesting that cold fronts are reliable signposts of cluster merger activity. 2.2.2 Relaxed Benchmark Cluster Sample Using the cold front signpost technique to distinguish between disturbed and relaxed clusters, we searched for clear cut cases of dynamically-relaxed clusters from the Chandra archive1. Clusters were initially selected according to the same selection criteria listed in Owers et al. (2009b) in terms of their total exposure time and redshift range. To satisfy the criteria, clusters must have: a total Chandra ACIS-I and/or ACIS-S exposure time exceeding 40 ks; a cluster redshift in the range 0.05 z 0.3. The Chandra X-ray images of these clusters were then inspected to identify those that had a dynamically-relaxed appearance, as manifested by a smooth, undisturbed, 1 http://cxc.cfa.harvard.edu/cda/

29. 2.3. Observing Program 15 Table 2.1 Observing program summary Name Telescope/ Observing Seeing Comments Instrument Dates () A963 MMT/Hectospec 17/02/1014/05/10 0.51.1 Observed in queue mode over 2 semesters A1650 AAT/AAOmega 08/06/1012/06/10 1.25.5 Clear on rst and last nights. Other nights cloudy. 28/07/1131/07/11 1.32.2 Generally cloud-free conditions A3667 20/05/0721/05/07 1.92.7 AATDA sample. 1000R grating in red arm. Some cloud? 14/07/0718/07/07 2.44.0 OCN09a sample. Generally cloud-free conditions. A3827 08/06/1012/06/10 1.25.5 Clear on rst and last nights. Other nights cloudy. 28/07/1131/07/11 1.32.2 Generally cloud-free conditions axisymetric X-ray surface brightness distribution. This yielded three clusters: Abell 963, Abell 1650 and Abell 3827. Their r-band images and X-ray surface brightness contours are shown in Figure 2.1 with X-ray surface brightness contours overplotted. These three clusters were adopted as our Relaxed Benchmark Cluster Sample. 2.3 Observing Program Large samples of optical spectra were obtained for galaxies in the clusters Abell 963, Abell 1650, Abell 3667 and Abell 3827. A1650, A3667 and A3827 were observed on the 3.9m Anglo-Australian Telescope (AAT) at Siding Spring Observatory over various runs in 2007, 2010 and 2011. A963 was observed on the 6.5m MMT at Mount Hopkins, Arizona, in queue mode over various nights between 17/02/2010 and 14/05/2010. The relevant details of our spectroscopic observing program are summarised in Table 2.1. The key observational properties of our cluster sample are summarised in Table 2.2. We adopt the right ascension and declination values of the cluster BCG for the coordinates of the clusters. Cluster redshifts, velocity dispersions and r200 values were calculated using our own galaxy redshift measurements and are discussed in more detail in Section 2.6. X- ray luminosities, LX, in the ROSAT 0.12.4 keV band and derived intracluster medium temperatures, kT, from Ebeling et al. (1996), have also been included for completeness. 2.4 AAT Observations Abell 3667 was observed on the AAT between 2007 July 1418. The details of the observations are discussed in depth in OCN09a, however, the key points are discussed in the text below. The conditions were generally cloud-free with 2.4 4.0 seeing during the run. Additional spectra for Abell 3667 were downloaded from the AAT Data Archive2 covering observations made between 2007 May 2021 on the Smith, Hudon & Haines observing program. The observing log for these nights quote a seeing of 1.92.7 for the A3667 eld. 2 http://apm5.ast.cam.ac.uk/arc-bin/wdb/aat database/observation log/make

30. 16 Chapter 2. Observations Figure 2.1 SDSS r-band images of the cores of Abell 963 (top panel) and Abell 1650 (middle panel) and SuperCOSMOS rF -band image of the core of Abell 3827 (bottom panel). Chandra X-ray brightness contours have been overplotted on these images. The contours are relatively smooth and elliptical in shape, indicating that the clusters are likely to be dynamically-relaxed.

31. 2.4. AAT Observations 17 Table 2.2 Summary of key cluster observational properties. The coordinates of the clusters are taken as the right ascension and declination of the BCG. Systemic redshift, z, characteristic velocity dispersion, z, and r200 values were calculated from our own galaxy redshift measurements. Uncertainties on these measurements are 16- and 84-percentile bootstrap condence intervals. ROSAT 0.12.4 keV band X-ray luminosity, LX, and intracluster medium temperature, kT, values are taken from Ebeling et al. (1996). The LX values were corrected for our new redshift measurements and our adopted cosmology. X-ray morphologies were determined by eye from Chandra X-ray images. Name RA Dec z z r200 LX kT Morphology (J2000) (J2000) (km/s) (Mpc) (1044 erg/s) (keV) A963 10 17 03.63 39 02 49.39 0.20373+0.00028 0.00035 939+54 56 2.100.12 6.08 8.4 Relaxed A1650 12 58 41.49 -01 45 41.25 0.08427+0.00039 0.00042 751+50 73 1.79+0.12 0.17 4.28 5.5 Relaxed A3667 20 12 27.35 -56 49 36.10 0.055350.00024 1014+44 49 2.44+0.11 0.12 5.10 6.5 Disturbed A3827 22 01 53.12 -59 56 45.04 0.09954+0.00021 0.00020 922+31 33 2.176+0.073 0.079 4.37 7.4 Relaxed According to the observing log, a number of observations over these 2 nights were stopped and restarted due to cloud in eld, hence the observations may have been partially af- fected by cloud cover. We will hereafter refer to the observations downloaded from the AAT Data Archive as the AATDA sample. Abell 1650 and Abell 3827 were observed over 2 runs between 2010 June 812 and 2011 July 2831. The conditions during the 2010 run was clear on the rst and last nights and cloudy with some rain on the other nights. There were large uctuations in seeing over the run, ranging from 1.25.5. The conditions were generally cloud-free during the 2011 run with seeing in the range 1.32.2. 2.4.1 Target Selection and Prioritisation To make our new Relaxed Benchmark Cluster Sample observations as similar as possible to each other as well as OCN09as observations of Abell 3667, we have tried as best as possible to use a consistent method of selecting and prioritising targets for observation. We have attempted to apply the method of OCN09a as far as practicably possible to all of our new observations, modulo small dierences between the parent photometric catalogue sources and observing conditions experienced during our observing runs. Details for each cluster now follow. Abell 1650 Spectroscopic targets were selected and prioritised in a similar manner as done by OCN09a for Abell 3667. Targets for this cluster were selected from the Sloan Digital Sky Survey

32. 18 Chapter 2. Observations (SDSS) Data Release 7 (DR7, Abazajian et al., 2009) within a 60 (5.7 Mpc) radius of the BCG at RA = 12h 58m 41.50s, DEC = -1 45 41.26. An initial cut was made to remove spectroscopically-conrmed stars and interlopers based on existing SDSS and Pimbblet et al. (2006) redshifts. Initial estimates of the cluster recession velocity and velocity dispersion were taken from Pimbblet et al. (2006) who measured 25,134 55 km s1 and 795+42 36 km s1 for the recession velocity and velocity dispersion, respectively. Targets with recession velocities greater than 2,400 km s1 ( 3) from the cluster recession velocity were rejected. SDSS g- and r-band magnitudes were converted to B- and R-band magnitudes using the colour transformations of Cross et al. (2004). Only photometrically-classied galaxies (SDSS PhotoPrimary type = 3) down to R 19.5 (rproj 3 Mpc) and R 18.5 (rproj > 3 Mpc) were included in the parent photometric catalogue. A faint magnitude limit of R = 19.5 was chosen to match the faint absolute magnitude limit used for Abell 3667 to probe 3 magnitudes down the luminosity function, i.e. down to M R + 3. To calculate the corresponding apparent faint magnitude limit we assumed a value of M R = 21.3 (Yagi et al., 2002) and a distance modulus of 37.9, ignoring any K-correction. Objects brighter than the BCG (R = 13.68) were also removed from the catalogue. A colour cut was applied to remove targets signicantly redder than the cluster colour-magnitude relation (CMR) and thus likely to be background objects. An upper red envelope of B-R = 2.14 was selected by eye as a good compromise between encompassing enough targets scattered redward of the CMR while also keeping the parent catalogue to a manageable size. Finally, the target positions were overlaid onto a SDSS r-band image of the cluster in DS9 to check for and remove false detections (usually diraction patterns) around bright stars. Targets were then prioritised according to their distance from the cluster core with the highest weights (priority = 9) given to targets in the inner 500 kpc and continuously lower weights were assigned to targets in subsequent 500 kpc annuli out to 3 Mpc. This strategy was employed to avoid spectroscopic incompleteness problems which arise when trying to assign bres to densely packed elds such as the core regions of galaxy clusters e.g. Yoon et al. (2008). Targets further than 3 Mpc from the cluster core were assigned the lowest possible weight (priority = 1) as a backup reservoir for bres which could not be assigned to an object in the more densely packed cluster core. These initial cuts resulted in a parent photometric catalogue of 1,336 targets, of which 806 were high priority targets within 3 Mpc of the cluster core. Fibres were then allocated to targets according to this

33. 2.4. AAT Observations 19 weighting scheme with the CONFIGURE3 (Miszalski et al., 2006; Robotham et al., 2010) software at the telescope. The colour magnitude diagram of the Abell 1650 parent photometric catalogue is shown in the top panel of Figure 2.2. A colour-magnitude relation (CMR) was tted to the red sequence of the SDSS spectroscopically-conrmed cluster members within 3 Mpc of the cluster core (open black circles). The CMR is represented by the dashed line in the Figure. The upper red envelope (horizontal dotted line) was chosen based on the position of the CMR. Also shown in this Figure is our ranking scheme, represented by dierent colour/size data points. The meanings of the dierent colour/size data points are detailed in the Figure caption. A conspicuous red sequence can be seen all the way down to R 18, and the highest priority targets (large red circles) tend to trace the CMR all the way down to the faint magnitude limit at R = 19.5. To observe the large number of spectroscopic targets in Abell 1650, the parent photometric catalogue was broken down into 4 dierent congurations during the 2010 observing run. Two bright congurations were devoted to targets brighter than R < 18.5 with the remaining 18.5 < R < 19.5 magnitude targets and unassigned bright targets allocated to 2 faint congurations. The bright-faint dividing magnitude at R = 18.5 and faint magnitude limit at R = 19.5 are represented by vertical dotted lines in Figure 2.2. One additional conguration was observed during the 2011 run, composed of targets not observed during the 2010 run as well as observed targets with low S/N spectra from the 2010 run. To attain sucient S/N, targets were observed over multiple nights at the same hour angle using the bre-locking capabilities of the CONFIGURE software. To maximise the number of targets observed during our run, we measured the redshifts of targets and cycled out those with recession velocities > 5000 km s1 from the clusters systemic recession velocity between nights as well as targets that had attained sucient S/N 10. We replaced the cycled out targets with unobserved targets from the parent photometric catalogue. Abell 3667 Targets for the Abell 3667 OCN09a spectroscopic observations were drawn from the Su- perCOSMOS Sky Survey (SSS) server within a 53 (3.4 Mpc) radius of the BCG at RA = 20h 12m 27.38s, DEC = -56 49 35.7. Only photometrically-classied galaxies (SSS class = 1) down to rF 19 (rproj 2 Mpc) and rF 18 (2 < rproj 3.4 Mpc) were included in the parent photometric catalogue. A faint apparent magnitude limit was imposed at 3 http://www.aao.gov.au/AAO/2df/aaomega/aaomega congure.html

34. 20 Chapter 2. Observations rF = 19, probing 3 magnitudes down the luminosity function i.e. down to M rF + 3. This faint magnitude was calculated assuming a value of M rF = 20.84 (Eke et al., 2004) and a distance modulus of 36.96, ignoring any K-correction due to the low-redshift of the cluster. Targets were then prioritised according to their projected distance from the BCG and position relative to the red sequence on the colourmagnitude plane. A more detailed description of the target prioritisation can be found in OCN09a. Because of the large number of galaxy targets and limitations on the separation of bres ( 30 arcsec; Miszalski et al., 2006) on the 2dF eld plate, six dierent congurations were required to obtain sucient spectroscopic completeness levels, especially in the central regions where cold fronts in the intracluster medium had been previously studied by Owers et al. (2009b). Fibres were allocated to targets using the CONFIGURE software at the telescope, with higher target prioritisation given to targets near the dense cluster core than in the cluster outskirts and also higher target prioritisation given to targets on or blueward of the red sequence. This strategy yielded high levels of radial spectroscopic completeness ( > 80% out to 2.5 Mpc) while giving low priority to targets redder than the red sequence which were likely to be background objects. The Abell 3667 AATDA spectra were taken in a single conguration integrated at the same hour angle over 2 nights but nothing is known about how the the targets were selected or prioritised for bre assignment. To the best of our knowledge, we are the rst to publish these observations. Abell 3827 Spectroscopic targets were selected and prioritised in a similar manner as for Abell 1650 and Abell 3667. Targets for this cluster were selected from the SuperCOSMOS Sky Archive (SSA) within a 55 (6 Mpc) radius of the BCG at RA = 22h 1m 53.04s, DEC = -59 56 44.88. An initial cut was made to remove spectroscopically-conrmed stars and foreground/background galaxies based on existing 2dF Galaxy Redshift Survey (2dFGRS; Colless et al., 2001) redshifts. Initial estimates of the cluster recession velocity and velocity dispersion were taken from Struble & Rood (1999) who derived values of 0.0984 (29,500 km s1) and 1,114 km s1 for the redshift (recession velocity) and velocity dispersion, respectively. Targets with recession velocities greater than 3,350 km s1 ( 3 z) from the cluster recession velocity were rejected. Photometric objects down to rF 20.5 were included in the parent photometric catalogue. Only photometrically-classied galaxies (SSA class = 1) were admitted down to rF 18.9. At fainter magnitudes we did not trust the SSA photometric star/galaxy seper-

35. 2.4. AAT Observations 21 ation and so we admitted all photometrically-classied galaxies and stars (SSA class = 2). A faint apparent magnitude limit of rF = 20.5 was chosen to match the faint absolute magnitude limit adopted for Abell 1650 and Abell 3667 that probed down to M rF + 3. This faint apparent magnitude limit was calculated assuming a value of M rF = 20.84 (the same as for Abell 3667) and a distance modulus of 38.31, ignoring any K-correction. Finally, the target positions were overlaid onto a SSA rF band image of the cluster in DS9 to check for and remove false detections around bright stars. Targets were then prioritised according to their distance from the cluster core and their colour relative to the red sequence. An upper red envelope of bJ -rF = 1.4 was selected by eye as a good compromise between encompassing enough targets scattered redward of the CMR while also keeping the parent catalogue to a manageable size. Targets in the inner 500 kpc with bJ -rF colours < 1.4 were given the highest weights (priority = 9) and continuously lower weights were assigned to targets in subsequent 500 kpc annuli with bJ -rF colour < 1.4 out to 3 Mpc. Targets with bJ -rF colours > 1.4 were also prioritised according to their distance from the cluster core but were given an overall lower weighting than the bJ -rF < 1.4 targets, starting with an intermediate weight (priority = 6) for the inner 500 kpc. Targets further than 3 Mpc from the cluster centre and brighter than rF 18.9 from the cluster core were assigned the lowest possible weight (priority = 1) as a backup reservoir for bres which could not be assigned to an object in the more densely packed cluster core. These initial cuts resulted in a parent photometric catalogue of 4,204 targets, of which 1,951 were high priority targets within 3 Mpc of the cluster core and with bJ -rF < 1.4. Fibres were then allocated to targets according to this weighting scheme with the CONFIGURE software at the telescope. The colour magnitude diagram of the Abell 3827 parent photometric catalogue is shown in the bottom panel of Figure 2.2. A colour-magnitude relation was tted to the red sequence of the 2dFGRS spectroscopically-conrmed cluster members within 3 Mpc of the cluster core (open black circles). The CMR is represented by the dashed line in the Figure. Also shown in this Figure is the target prioritisation scheme, represented by the dierent colour/size data points. The meanings of the dierent colour/size data points are discussed in the Figure caption. A dominant red sequence can be seen down to rF 19, however, it begins to get lost in the scatter at fainter magnitudes, hence the SSA star/galaxy separation was only applied down to rF = 18.9. The parent photometric catalogue was broken down into 4 dierent congurations during the 2010 observing run. Two bright congurations were devoted to targets brighter than rF < 18.9 with the remaining 18.9 < rF < 20.5 magnitude targets and unassigned

36. 22 Chapter 2. Observations bright targets allocated to 2 faint congurations. Four additional congurations were observed during the 2011 run, composed of targets not observed during the 2010 run as well as observed targets with low S/N (i.e. < 10) spectra from the 2010 run. The brightfaint dividing magnitude at rF = 18.9 and faint magnitude limit at rF = 20.5 are represented by vertical dotted lines in Figure 2.2. To attain sucient S/N and maximise the number of targets observed, we employed the same bre-locking and cycling-out strategy as for Abell 1650. 2.4.2 Telescope and Instrumentation Spectroscopic observations of target galaxies were taken with the Two Degree Field(2dF)/AA multi-object spectrograph (MOS) instrument on the 3.9m Anglo-Australian Telescope (AAT). The instrument, AA, is a bench-mounted dual-beam spectrograph which is fed by 400 2 aperture bers which are placed within the AATs two degree eld of view at prime-focus by the 2dF robotic bre positioner (Saunders et al., 2004; Smith et al., 2004b; Sharp et al., 2006). Eight bres are dedicated to guide stars, with the remaining 392 dedicated to a mixture of science targets and sky positions. At the redshifts of our clusters, the 2 bres correspond to physical scales of 3.2 kpc, 2.1 kpc and 3.6 kpc for Abell 1650, Abell 3667 and Abell 3827, respectively. All AAT observations except for the Abell 3667 AATDA observations were taken using the medium resolution 580V (blue arm) and 385R (red arm) gratings, delivering a spectral resolution at the detectors of 3.5 A (FWHM) in the blue and 5.3 A (FWHM) in the red spanning a continuous, combined wavelength range of 3700 8800 A spliced together at 5700 A. The A3667 AATDA observations were taken using a combination of the 580V grating in blue arm and the higher resolution 1000R grating delivering a spectral resolution of 1.9 A (FWHM) in the red arm. The AATDA spectra are unspliced because the selected gratings do not cover the spectral range between 55006000 A. 2.4.3 Data Reduction All the AAT data were reduced using version 4 of the 2DFDR4 (Sharp & Birchall, 2010; Sharp & Parkinson, 2010) data reduction pipeline. The data reduction pipeline yielded fully at-elded, wavelength-calibrated (but not ux-calibrated) spectra and variance arrays. The data reduction pipeline also co-added individual frames, applied telluric- corrections and performed dark, bias, and sky subtraction on the raw data. Sky subtraction was achieved by assigning a minimum of 2530 bres to blank sky positions for 4 http://www.aao.gov.au/2df/aaomega/aaomega 2dfdr.html

37. 2.4. AAT Observations 23 Figure 2.2 Colour-magnitude diagrams of the parent photometric catalogues for Abell 1650 (top panel) and Abell 3827 (bottom panel). The dashed lines show the best t to the red sequence based on the spectroscopically-conrmed cluster members (open black circles). Our target prioritisation scheme is represented by the colour and size of the data points in the Figure. The large/red points were assigned the highest priority, the medium-size/green points were assigned intermediate priority, the small/blue points were assigned the lowest priority while the small/grey points were rejected based on our selection criteria. The horizontal, dotted line shows the upper red envelope adopted for each cluster. The left- most vertical dotted lines show the bright-faint dividing magnitude and the right-most vertical dotted lines show the faint magnitude limit adopted for each cluster. The faint magnitude limit adopted for these clusters corresponds to M + 3.

38. 24 Chapter 2. Observations each of our congurations. The sky bre positions were manually checked by eye in DS9 to avoid placing bres on objects. Tungsten and arc lamp exposures were taken at the start of each new conguration for the purposes of at-elding and wavelength calibration except for a few cases when they were taken at the end of a conguration. Bias frames were taken at the start of each observing run and dark frames were acquired at the end of some nights and also downloaded from the AAT archive for other nights close in time to our observing run. We encountered some problems splicing the blue and red arms of some of our AAT spectra using 2DFDR. The problem mainly aected the low S/N spectra and our initial investigations hinted that the problem was related to poor scattered light subtraction and throughput measurements. To address the issue, the blue and red arms of all the AAT data (except for the Abell 3667 AATDA sample) were spliced together using our own customised software written in the IDL programming language. Our splicing software performed 2 tasks on the unspliced spectra: i) the shape of each spectrum was corrected by dividing it by the respective transfer functions for the 580V and 385R gratings; and ii) the red arm spectra were scaled so that the average ux in the overlapping wavelength regions around 5700 A matched the average ux in the same wavelength region of the blue arm spectra. Some examples of our customised, spliced spectra are shown in Figure 3.1. 2.4.4 Redshift Measurements Redshifts for the AAT clusters Abell 1650, Abell 3667 (OCN09a sample) and Abell 3827 were measured using the semi-automated RUNZ5 software written by Will Sutherland for the 2dFGRS. The Abell 3667 AATDA redshifts were measured using the FXCOR routine in IRAF. Both RUNZ and FXCOR use the cross-correlation method of Tonry & Davis (1979) to estimate the most-likely value for the redshift. All spectra were manually inspected by eye, as part of the redshift process, to check the validity of (and override where necessary) the automated redshift measurements. The spectra were then assigned a redshift quality ag by the user, based on the number of visually identied lines in the spectrum. In this scheme, integer values between 1 5 are reserved for extragalactic objects, with quality 3 considered to be a reliable redshift based on the visual identication of 2 or more lines, while quality 2 are unreliable. Quality = 6 is reserved for conrmed Milky Way stellar objects. Our nal AAT redshift measurements and their uncertainty measurements are summarised in Table 3.7. 5 http://www.physics.usyd.edu.au/scroom/runz

39. 2.4. AAT Observations 25 Abell 1650 Of the objects that were observed multiple times, 249 galaxies were observed twice and 8 galaxies were observed three times. This resulted in a set of 273 repeat measurements that could be used to check the consistency and accuracy of our Abell 1650 measurements. We also found 201 SDSS Data Release 9 (DR9, Ahn et al., 2012) galaxy redshifts in common with our AAT Abell 1650 measurements resulting in a set of 130 external repeat measurements (a large fraction of the SDSS measurements correspond to double and triple measurements in the AAT sample). In the top panel of Figure 2.3 we show our repeat recession velocity measurements for Abell 1650. The ordering of the AATAAT repeat recession velocity measurements (black data points) was randomised, in the sense that the ordering of the x- and y-values in the (x,y) pairs is random. The AATSDSS external recession velocity comparison (red data points) was plotted with the SDSS DR9 measurements on the x-axis and our AAT measurements on the y-axis. To remove outliers from the Figure, we have not plotted recession velocity measurements corresponding to spectra with S/N < 3; these measurements tend to be scattered far from the one-to-one relation. The internal and external repeat measurements are tightly correlated about the one-to-one relation shown by the diagonal dotted line. This demonstrates that our AAT recession velocity measurements are both self-consistent and consistent with SDSS measurements. In the bottom panel, we show the residuals about the one-to-one relation of our repeat recession velocity measurements. We initially measured a variance-weighted oset of 20.9 3.8 km s1 in the AATSDSS residuals with our AAT measurements higher than the SDSS measurements. To put our recession velocity measurements on the same system as SDSS DR9, we subtracted 20.9 km s1 from our raw measurements and propagated the systematic error into the updated recession velocity uncertainties. The measurements shown in Figure 2.3 and presented in Table 3.7 have been corrected for this oset. After correcting our AAT recession velocity measurements, we measured a variance-weighted RMS of 82.2+4.3 4.9 km s1 in the AATAAT residuals. From this value we report a repeat measurement recession velocity uncertainty of 58.2+3.0 3.4 km s1 for Abell 1650. The uncertainties on these measurements correspond to the 16- and 84-percentiles of the probability distributions of the measurements, obtained using the bootstrap technique. The technique is described in great detail in Efron (1981) but, briey, the probability distribution is obtained by randomly selecting n data points from the empirical distribution of measurements, where n is equal to the size of the original distribution. The empirical distribution is resampled as many times as practicably possible, performing the original

40. 26 Chapter 2. Observations measurement over and over again, until a measurement probability distribution is built up; the measurement uncertainties are estimated from the resultant probability distribution. In this case, the probably distributions were generated with 106 Monte Carlo (MC) simulations. We will refer to this method hereafter as bootstrapping. Abell 3667 Comparing the OCN09a sample to the AATDA sample we found 212 galaxies with one OCN09a and one matching AATDA observation and 24 galaxies with two OCN09a and one matching AATDA observation. This resulted in a set of 260 repeat measurements to check the consistency and accuracy of our new AATDA redshift measurements in comparison to the OCN09a sample. We also found 88 NOAO Fundamental Plane Survey (NFPS, Smith et al., 2004a) galaxy redshifts in common with our combined OCN09a and AATDA Abell 3667 measurements resulting in a set of 170 external repeat measurements (many of the NFPS measurements correspond to double and triple measurements in the combined AAT sample). In the top panel of Figure 2.4 we show our repeat recession velocity measurements for Abell 3667. Unlike Figure 2.3, the AATAAT repeat recession velocity measurements (black data points) were ordered with our AATDA measurements on the x-axis and OCN09a measurements on the y-axis. We deliberately did not randomise the pairs so that we could search for systematic osets between the AATDA and OCN09a measurements. The AATNFPS external recession velocity comparison (red data points) was plotted with the NFPS measurements on the x-axis and our AAT measurements on the y-axis. To remove outliers from the gure, we have not plotted recession velocity measurements corresponding to to spectra with S/N < 3; these measurements tend to be scattered far from the one-to-one relation. This demonstrates that our AAT recession velocity measurements are both self-consistent and consistent with NFPS measurements. Smith et al. (2004a) report an oset of 11 km s1 between their NFPS recession velocity measurements and those of SDSS DR2 (Abazajian et al., 2004). They also report an RMS scatter of 30 km s1 in a sample of 203 repeat measurements, from which we calculate a standard error of 2.1 km s1. To put the NFPS measurements onto the SDSS system we subtracted 11 km s1 from their raw measurements and propagated the systematic error into their updated recession velocity uncertainties. This allows us to calibrate our recession velocity measurements to SDSS, indirectly, via NFPS. In the bottom panel, we show the residuals about the one-to-one relation of our repeat recession velocity measurements. We initially measured a variance-weighted oset of

41. 2.4. AAT Observations 27 55.6+5.6 5.9 km s1 in the OCN09aAATDA residuals with the OCN09a measurements higher than the AATDA measurements. We empirically corrected our raw AATDA recession velocities by 55.3 km s1 to make them consistent with the OCN09a sample and propagated the systematic error into the updated AATDA recession velocity uncertainties. After correcting the OCN09aAATDA recession velocity oset, we measured a variance-weighted oset of 58.0+4.6 4.3 km s1 in the AAT-NFPS residuals with the AAT values higher. The oset is similar in magnitude to the OCN09aAATDA oset and since the NFPS redshifts were also measured in IRAF this suggests that RUNZ measures redshifts that are systematically 50 km s1 greater than IRAF. To put our recession velocity measurements on the same system as SDSS (via NFPS) we subtracted 58.0 km s1 from our raw measurements and propagated the systematic error into the updated recession velocity uncertainties. The measurements shown in Figure 2.4 and presented in Table 3.7 have been corrected for these osets. After correcting our AAT recession velocity measurements, we measured a variance-weighted RMS of 89.9+3.9 4.2 km s1 in the OCN09aAATDA residuals. OCN09a report a recession velocity uncertainty of 107 km s1 for the OCN09a sample, hence the recession velocity uncertainty of the AATDA sample is no greater than 63.6+2.8 3.0 km s1, reecting the higher overall S/N of the AATDA spectra over the OCN09a spectra. The uncertainties on these measurements correspond to the 16- and 84-percentiles of the probability distributions of the measurements. The uncertainties were calculated by bootstrapping, as described above for Abell 1650. Abell 3827 Of all the Abell 3827 galaxies we observed on the AAT, 234 galaxies were observed twice and 50 galaxies were observed three times. This resulted in a set of 384 internal repeat measurements to check the consistency and accuracy of our A3827 measurements. The ordering of the repeat cz measurements was randomised as was done for Abell 1650. We also found 15 NASA Extragalactic Database6 (NED) galaxy redshifts in common with our A3827 measurements resulting in a set of 29 external repeat measurements (nine of the NED measurements correspond to double measurements and one of the NED measurements corresponds to a triple measurements in the AAT sample). The NED redshifts were taken from many sources, however the majority were taken from the Two Micron All Sky Survey7 (2MASS) and Automated Plate Measurement United Kingdom Schmidt (APMUKS, see Maddox et al., 1990) sources with redshifts from Katgert et al. (1998) 6 http://ned.ipac.caltech.edu/ 7 http://www.ipac.caltech.edu/2mass/

42. 28 Chapter 2. Observations Table 2.3 Analysis of AAT recession velocity repeat measurements. The columns from left to right summarise (i) cluster name; (ii) recession velocity uncertainty from internal repeat measurements; (iii) number of data points in internal comparison; (iv) recession velocity oset in external comparison; (v) number of data points in external comparison. The recession velocity osets were added to our raw measurements to put them on the SDSS system. Name Error (km/s) nint Oset (km/s) next A1650 58.2+3.0 3.4 227 20.9 3.8 201 A3667 63.6+2.8 3.0 276 58.0+4.6 4.3 169 A3827 86.7+4.4 4.8 366 20.9 3.8 27 and one galaxy, ESO 146-IG 005, with a redshift from Corwin & Emerson (1982). The individual redshift uncertainties for the NED measurements were not specied, however, we adopted a uniform uncertainty of 83 km s1 which resulted in a reduced 2 1 when combined with our AAT measurements. In the top panel of Figure 2.5 we show our repeat recession velocity measurements and in the bottom panel, recession velocity residuals. The recession velocity measurements in this plot were cleaned with a S/N > 3 lter to remove discrepant points. The diagonal dotted line in the top panel is the one-to-one relation, not a line tted to the data. The repeat measurements are strongly clustered about the one-to-one relation indicating that the our measurements are self-consistent and roughly consistent with the NED measurements. We measured a large, variance-weighted oset of 116+32 18 km s1 in the AAT-NED residuals with our AAT measurements lower than the NED measurements. However, we made no attempt to correct our raw AAT redshift measurements with the NED measurements due to the inhomogeneity and unknown measurement uncertainties of the NED sample. In- stead, we applied the recession velocity corrections from Abell 1650, which is the nearest cluster in redshift and was observed under the same conditions and over the same observing runs as Abell 3827. Hence, we put our recession velocity measurements on the same system as SDSS DR9 by subtracting 20.9 3.8 km s1 from our raw measurements and propagating the systematic error into the updated velocity dispersion uncertainties. The measurements shown in Figure 2.5 and presented in Table 3.7 have been corrected for this oset. In the AATAAT residuals, we measured a variance-weighted RMS of 122.6+6.3 6.9 km s1. From this value we report a repeat measurement recession velocity uncertainty of 86.7+4.4 4.8 km s1 for A3827. The uncertainties on these measurements correspond to the 16- and 84-percentiles of the probability distributions of the measurements. The uncertainties were calculated by bootstrapping, as described for Abell 1650.

43. 2.4. AAT Observations 29 Figure 2.3 Abell 1650 recession velocity comparison (top panel) and recession velocity residuals (bottom panel). In the top panel, the black data points represent repeat observations within our AAT data set. The ordering of the cz measurements is random as described in the text. The red data points represent SDSS DR9 redshift measurements on the x-axis compared to our AAT redshift measurements on the y-axis. Error bars are shown on all measurements. The one-to-one relation is represented by the diagonal dotted black line. The bottom panel shows the velocity dispersion residuals using the same colour scheme as in the top panel with residual uncertainties summed quadratically.

44. 30 Chapter 2. Observations Figure 2.4 Abell 3667 recession velocity comparison (top panel) and recession velocity residuals (bottom panel). In the top panel, the black data points represent repeat observations between our AAT data sets. The cz measurem