Dynamically-Driven Galaxy Evolution in Clusters of Galaxies
853
Dynamically-Driven Galaxy Evolution in Clusters of Galaxies Peter Christian Jensen Presented in fulfilment of the requirements of the degree of Master of Science 2014 Faculty of Science, Engineering and Technology Swinburne University of Technology
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 interac- tions, while up to 5% of
the cluster members could have experienced an earlier round of
starbursts, triggered at the time of core passage.
3. ii
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
spec- troscopic 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.
6. v
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
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 measure- ments for all star-forming
galaxies in our data set . . . . . . . . . . . . . . . 100 3.19
Star formation rate, specic star formation rate and Scalo birthrate
param- eter 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 member- ship 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 mea- surements 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 spectro- scopic classication frequencies
within 1.32 r200 . . . . . . . . . . . . . . . . 125 4.4 Comparison
between Abell 3667 and LARCS galaxy counts and spectro- scopic
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 forma- tion 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 astro- metric,
photometric, kinematic and spectroscopic signal-to-noise ratio mea-
surements . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 180 A.2 Catalogue of D4000 and He index
measurements and Lick index measure- ments 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 com-
plementary 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 red-
shift (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 sub- structures 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
separa- tion 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 detec- tion 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
sig- natures 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 coinci- dent 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 res- olution 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 subclus- ters,
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 mem- bers, 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 mor- phologies 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 pres- sure
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
distribu- tion 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 activ- ity 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 ex- ample, 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 sys- tem, 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), mas- sive ( = 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);
detec- tion 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
sub- structures, 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 dicul- ties
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 provid- ing 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 compar-
ison 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 pro- cessed
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 forma- tion 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 opti- cal,
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 selec- tion
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 ob- serving program; (iii)
how we obtained and processed our raw Anglo-Australian Telescope
and MMT observations, including in-depth discussions about target
selection and priori- tisation 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 obser- vations 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 clus- ters 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 in- tracluster 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
photo- metric 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 ob- served
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 re- cession 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 cata- logue. 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
dur- ing 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 sub- traction
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 ob- jects, 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 measure- ments 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 re- peat 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 re- peat
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 prob- ability distributions of
the measurements, obtained using the bootstrap technique. The
technique is described in great detail in Efron (1981) but, briey,
the probability distri- bution 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 distribu- tion. 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 measure- ments
(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 mea- surements. 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 mea- surements 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 re- peat 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 ve- locities 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 cor- recting
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 red- shifts
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 measure- ments 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 un- certainties. 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 spec-
tra. 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 mea- surements
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 dot- ted 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 observ- ing
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 ob- servations 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 ob- servations between our AAT data sets. The cz
measurem