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The Evolution of Star Formation Activity in Cluster Galaxies Over 0.15 < z < 1.5 by Cory R. Wagner A thesis submitted to the Department of Physics, Engineering Physics & Astronomy in conformity with the requirements for the degree of Master of Science Queen’s University Kingston, Ontario, Canada September 2014 Copyright © Cory R. Wagner, 2014 Abstract In this thesis, we explore 7.5 billion years of evolution in cluster galaxy star formation activity using a sample of 11 high-redshift (1 < z < 1.5) clusters from the IRAC Shallow Cluster Survey, and 25 low-redshift (0.15 < z < 1) clusters from The Cluster Lensing And Supernova survey with Hubble. We compare cluster galaxy star formation to that of the field over 0.15 < z < 1.5 using ∼8000 galaxies from the UltraVISTA survey. Mid-infrared star formation rates are measured using Spitzer 24 µm data for isolated high-redshift galaxies. We calculate rest-frame ultraviolet star formation rates for low-redshift cluster members using Hubble Space Telescope observations. Using publically available mid-infrared and ultraviolet data for our field sample, we empirically derive scaling relations to adjust low-redshift cluster galaxy ultraviolet star formation rates to mid-infrared levels. We classify cluster galaxy morphology by visual inspection, and use quantitatively measured morphologies for field galaxies. Cluster late-type galaxies at z > 1 show enhanced star formation activity relative to the field, and account for nearly 90% of the overall star formation activity in high-redshift clusters. While high-redshift early-type galaxies are substantially quenched relative to cluster late-types, they still contribute ∼13% of the total cluster star formation activity. With early-type fractions increasing from 34 to 56% from z ∼ 1.5 → 1.16, we find that new cluster early-type galaxies are likely being formed i around z ∼ 1.4. The fraction of early-type galaxies that are star-forming drops from 29 to 11% over this period, yet their specific star formation rates are roughly constant. These factors suggest that the events that created these new galaxies, possibly mergers, were both recent and gas-rich. With typical coverages of 50% of z < 1 cluster virial radii, we can only probe the cores of low-redshift clusters. We find that in this regime, the star formation activity of cluster galaxies is quenched relative to the field. We compare the mean star formation rate of cluster galaxies to the results of Alberts et al. (2014), who fit the mean star formation rate evolution over 0.3 < z < 1.5, and measured star formation rates by stacking 250 µm Herschel images. We find excellent agreement between the Herschel-based fit and both our Spitzer-derived and ultraviolet→infrared star formation rates. ii Statement of Co-Authorship The research presented in this thesis was done under the supervision of Stéphane Courteau (Queen’s University), and Mark Brodwin (University of Missouri-Kansas City). All the work presented here was done by the author (Cory R. Wagner) except where explicitly stated otherwise. Chapter 2 contains a version of a paper submitted to The Astrophysical Journal entitled: “Star Formation in High-Redshift Cluster Ellipticals” by Cory R. Wagner, Mark Brodwin, Gregory F. Snyder, Anthony H. Gonzalez, S. A. Stanford, Stacey Alberts, Alexandra Pope, Daniel Stern, Gregory R. Zeimann, Ranga-Ram Chary, Arjun Dey, Peter R. M. Eisenhardt, Conor L. Mancone, and John Moustakas. I am the lead author on this paper, and performed all the analysis, wrote the manuscript, and created all the figures and tables. All observations and reductions were performed by others. Gregory F. Snyder provided the red-sequence data from Snyder et al. (2012). Mark Brodwin provided IRAC Shallow Cluster Survey data, and star forming fractions from Brodwin et al. (2013). Stacey Alberts provided mean star formation rates from Alberts et al. (2014), and attempted to derive 250 µm star formation rates for the IRAC Shallow Cluster Survey sample in this paper. S. A. Stanford, Anthony H. Gonzalez, and Daniel Stern assisted with morphological classifications on a subset of ISCS member galaxies. iii Acknowledgments Stéphane Courteau, for his encouragement. Mark Brodwin, for a great project. Mike McDonald, for his insights during the latter half of this thesis. Dan McIntosh, for introducing me to the world universe of astrophysics. My thesis defense committee (Stéphane Courteau, David Hanes, Danilo Marchesini, Alastair McLean) for their time and effort in reading through this thesis, and providing valuable comments that enhanced the quality of the work herein. This work is based in part on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech. Support for HST programs 10496, 11002, 11597, and 11663 were provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This work is based in part on observations obtained with the Chandra X-ray Observatory, under contract SV4-74018, A31 with the Smithsonian Astrophysical Observatory which operates the Chandra X-ray Observatory for NASA. iv Contents Abstract i Statement of Co-Authorship iii Acknowledgments iv Table of Contents v List of Tables viii List of Figures ix List of Common Acronyms xii Chapter 1: Introduction 1.1 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Do Early-Type Galaxies Share in the Wealth of Star Formation Activity in High-Redshift Clusters? . . . . . . . . . . . . . . . . . . . . . . 1.3 Low-Redshift: Where Have All The Star Formers Gone? . . . . . . . Chapter 2: 2.1 2.2 2.3 Paper I: Star Formation in High-Redshift lipticals Paper I Organization . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 IRAC Shallow Cluster Survey . . . . . . . . . . 2.2.2 HST Data . . . . . . . . . . . . . . . . . . . . . 2.2.3 Mid-Infrared Data . . . . . . . . . . . . . . . . 2.2.4 Chandra X-ray Data . . . . . . . . . . . . . . . Galaxy Selection Method . . . . . . . . . . . . . . . . . 2.3.1 Identification of Cluster Members . . . . . . . . 2.3.2 Rejection of AGNs . . . . . . . . . . . . . . . . 2.3.3 Stellar Masses and Mass Limit . . . . . . . . . . v 1 1 2 4 Cluster El. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 6 7 7 7 9 10 10 11 11 12 . . . . . . . . . . . . . 12 13 16 17 17 18 20 23 25 26 28 30 34 . . . . . . . 37 37 38 39 39 42 43 47 . . . . . 48 49 49 50 52 53 Chapter 5: Infrared-Ultraviolet Star Formation Rate Comparison 5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Empirical Fits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 56 60 Chapter 6: Analysis 6.1 Projected Radius . . . . . . . . . . . . . . . . . . . . . 6.2 Galaxy Morphology Versus Projected Radius . . . . . . 6.3 Star Formation Rate Versus Projected Radius . . . . . 6.4 Mean Star Formation Rate Versus Projected Radius . . 6.5 Specific Star Formation Rate Versus Projected Radius . 6.6 Galaxy Morphology Versus Redshift . . . . . . . . . . . 63 63 63 65 67 70 72 2.4 2.5 2.6 2.3.4 Isolation . . . . . . . . . . . . . . . . . . . . . . 2.3.5 Morphology . . . . . . . . . . . . . . . . . . . . 2.3.6 Comparison Field Sample Selection . . . . . . . Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Star Formation Rate vs. Radius . . . . . . . . . 2.4.2 Mean Star Formation Rate . . . . . . . . . . . . 2.4.3 Fraction of Star-forming Galaxies . . . . . . . . 2.4.4 Specific Star Formation Rate . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Comparison to Brodwin et al. (2013) . . . . . . 2.5.2 Comparison to Alberts et al. (2014) . . . . . . . 2.5.3 Star Formation in High-Redshift Cluster ETGs Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 3: Cluster Galaxy Data and Sample Selection 3.1 High-Redshift ISCS Clusters . . . . . . . . . . . . . . . . 3.2 Low-Redshift CLASH Clusters . . . . . . . . . . . . . . . 3.2.1 Redshifts . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Stellar Masses . . . . . . . . . . . . . . . . . . . . 3.2.3 Star Formation Rates . . . . . . . . . . . . . . . . 3.2.4 Cluster Membership . . . . . . . . . . . . . . . . 3.2.5 Galaxy Morphology . . . . . . . . . . . . . . . . . Chapter 4: Field Galaxy Data and Sample Selection 4.1 Redshifts . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Star Formation Rates . . . . . . . . . . . . . . . . . . 4.3 Stellar Masses . . . . . . . . . . . . . . . . . . . . . . 4.4 Galaxy Morphology . . . . . . . . . . . . . . . . . . . 4.5 Stellar Masses of Final Galaxy Samples . . . . . . . . vi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 6.8 Mean Star Formation Rate Versus Redshift . . . . . . . . . . . . . . . Specific Star Formation Rate Versus Redshift . . . . . . . . . . . . . 75 79 Chapter 7: Summary and Conclusions 7.1 What’s Next? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 86 Bibliography 88 vii List of Tables 2.1 ISCS Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1 CLASH Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.1 Coefficients of z < 0.1 fits of IR-derived SFR verus UV-derived SFR . 59 6.1 Comparison Cluster ETG Fractions From the Literature . . . . . . . 73 viii List of Figures 2.1 500 ×500 cutouts of five isolated high-redshift cluster ETGs . . . . . . . 14 2.2 SFR versus cluster-centric radius for high-redshift ETGs and LTGs . 17 2.3 Mean SFR versus cluster-centric radius for high-redshift cluster ETGs and LTGs, and mean SFR of high-redshift field ETGs . . . . . . . . . 2.4 19 Fraction of star-forming galaxies versus projected radius for high-redshift cluster ETGs, and low-redshift cluster galaxies, and fraction of starforming high-redshift field galaxies . . . . . . . . . . . . . . . . . . . 2.5 21 Specific SFR versus radius for high-redshift cluster ETGs and lowredshift cluster galaxies, and sSFR of high-redshift field ETGs . . . . 24 2.6 Fraction of star-forming ISCS cluster galaxies versus redshift . . . . . 27 2.7 Mean SFR versus redshift for ISCS cluster galaxies . . . . . . . . . . 29 2.8 Upper panel: Specific SFR as a function of redshift for ISCS cluster ETGs, and field ETGs and LTGs. Lower panel: ETG fraction versus redshift for ISCS clusters . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 32 Redshifted rest-frame UV window, and rest-frame g- and i-band filters. Best-fit observed-frame HST filters. Upper panel: z = 0.348 Lower panel: z = 0.686 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 41 3.2 Distribution of number of observed filters, and number of filters with 5 σ detections, for spectroscopically confirmed CLASH members. . . . 44 3.3 Spectroscopic and photometric redshift distributions for CLASH clusters. 46 4.1 Comparison between stellar masses derived with SED fitting and colormass-to-light ratios for UltraVISTA galaxies. . . . . . . . . . . . . . . 4.2 51 Stellar mass and median stellar mass versus redshift for CLASH and ISCS cluster galaxies, and UltraVISTA field galaxies, of varying morphologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 54 IR-derived SFR versus UV-derived SFR for z < 0.1 galaxies from UltraVISTA, Rosa-González et al. (2002), and Iglesias-Páramo et al. (2004) 58 5.2 IR-derived SFRs versus UV-derived SFRs for 0.15 < z < 1 COSMOS/UltraVISTA field galaxies . . . . . . . . . . . . . . . . . . . . . 5.3 Linear least squares fits to IR-derived SFR versus UV-derived SFR for 0.15 < z < 1 UltraVISTA field galaxies, binned by redshift . . . . . . 6.1 60 61 ETG fractions versus cluster-centric radius for CLASH and ISCS clusters. ETG fractions for 0.15 < z < 1 and 1 < z < 1.5 UltraVISTA field galaxies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.2 SFR versus cluster-centric radius for CLASH ETGs and LTGs . . . . 66 6.3 Mean SFR versus cluster-centric radius for CLASH and ISCS cluster galaxies, of varying morphologies. Mean SFR of 0.15 < z < 1 and 1 < z < 1.5 UltraVISTA field galaxies, of varying morphologies . . . . x 68 6.4 Specific SFR versus cluster-centric radius for CLASH and ISCS galaxies, of varying morphologies. Specific SFR for 0.15 < z < 1 and 1 < z < 1.5 UltraVISTA field galaxies, of varying morphologies . . . . 6.5 ETG fraction versus redshift for CLASH and ISCS clusters, UltraVISTA field, and comparison clusters from the literature . . . . . . . . 6.6 72 Mean SFR versus redshift for CLASH and ISCS cluster galaxies, and UltraVISTA field galaxies . . . . . . . . . . . . . . . . . . . . . . . . 6.7 71 76 Mean SFR versus redshift for CLASH, ISCS, and UltraVISTA galaxies of all morphologies, compared to fits of field and cluster mean SFR versus redshift from Alberts et al. (2014) . . . . . . . . . . . . . . . . 6.8 Specific SFR versus redshift for CLASH and ISCS cluster galaxies, and UltraVISTA field galaxies . . . . . . . . . . . . . . . . . . . . . . . . 6.9 78 80 Specific SFR versus redshift for CLASH, ISCS, and UltraVISTA galaxies of all morphologies, compared to fits of field and cluster sSFR versus redshift from Alberts et al. (2014), and a fit of field sSFR versus redshift from Elbaz et al. (2011) . . . . . . . . . . . . . . . . . . . . . . . xi 81 List of Common Acronyms ΛCDM: Lambda Cold Dark Matter ACS: Advanced Camera for Surveys AGN: Active galactic nucleus CLASH: Cluster Lensing And Supernova survey with Hubble CMLR: Color-mass-to-light relation CMR: Color-magnitude relation COSMOS: Cosmic Evolution Survey ETG: Early-type galaxy HST: Hubble Space Telescope IMF: Initial mass function IR: Infrared IRAC: Infrared Array Camera ISCS: IRAC Shallow Cluster Survey xii LTG: Late-type galaxy MIPS: Multiband Imaging Photometer for Spitzer MIR: Mid-infrared flux M/L: Mass-to-light NDWFS: NOAO Deep Wide-Field Survey NIR: Near-infrared NOAO: National Optical Astronomy Observatory SDWFS: Spitzer Deep, Wide-Field Survey SED: Spectral energy distribution SFH: Star-formation history SFR: Star formation rate S/N: Signal-to-noise sSFR: Specific star formation rate TP-AGB: Thermally pulsing asymptotic giant branch UV: Ultraviolet WFC3: Wide Field Camera 3 WFPC2: Wide Field Planetary Camera 2 WMAP7: Seven-Year Wilkinson Microwave Anisotropy Probe xiii Chapter 1 Introduction 1.1 Thesis Organization This thesis is organized into two parts. The first (Chapter 2) is based on a manuscript (Paper I), “Star Formation in High-Redshift Cluster Ellipticals,” submitted to The Astrophysical Journal. It focuses solely on the 1 < z < 1.5 cluster regime, 7.8 to 9.4 Gyr ago. Part II (Chapter 3 through Chapter 6) forms the basis of what will become Paper II, and with cluster observations spanning 0.15 < z < 1.5, we are able to extend the morphology driven investigation described in Chapter 2 down to ∼2 Gyr ago. For clarity, and to avoid significant overlap in the material covered, we absorb the majority of Paper I’s introduction here into Chapter 1 of this thesis. We also note that the references in Paper I will be contained in the bibliography at the end of this thesis. Throughout this work, we use AB magnitudes unless otherwise indicated. We adopt a WMAP7 cosmology (Komatsu et al. 2011), with (ΩΛ , ΩM , h) = (0.728, 0.272, 0.704). 1 1.2. DO EARLY-TYPE GALAXIES SHARE IN THE WEALTH OF STAR FORMATION ACTIVITY IN HIGH-REDSHIFT CLUSTERS? 1.2 Do Early-Type Galaxies Share in the Wealth of Star Formation Activity in High-Redshift Clusters? In the local Universe galaxy clusters are primarily populated by quiescent, early-type galaxies (ETGs) with little ongoing star formation and evolved stellar populations (Oemler 1974; Dressler 1980; Caldwell et al. 1993; Gómez et al. 2003; Bressan et al. 2006; Clemens et al. 2009; Edwards & Fadda 2011). Studies of cluster galaxy populations to z . 1 find that the evolution in the color and scatter of cluster red-sequences is consistent with simple passive evolution models in which the bulk of galaxies’ stars formed in a short, high-redshift starburst (Bower et al. 1992; Aragon-Salamanca et al. 1993; Stanford et al. 1998; Kodama 1999; Blakeslee et al. 2006; Mei et al. 2006, 2009; Eisenhardt et al. 2008; Muzzin et al. 2008). However, ΛCDM predicts a more extended, hierarchical formation history. For instance, simulations by De Lucia et al. (2006) find that only ∼50% of massive elliptical galaxies would have formed the bulk (80%) of their stellar mass by z ∼ 1.5. Recently, infrared (IR) measurements of the z > 1 cluster population have revealed substantial dust-obscured star formation activity (Hilton et al. 2010; Tran et al. 2010; Santos et al. 2013; Brodwin et al. 2013, hereafter B13). Alberts et al. (2014, hereafter A14) found that dust-obscured star formation in cluster galaxies increases with lookback time from z = 0.3 → 1.5. Several recent studies of the high-redshift (z > 1) clusters have been conducted using the IRAC Shallow Cluster Survey (ISCS; Eisenhardt et al. 2008). Mancone et al. (2010, 2012) measured the rest-frame near-infrared (NIR) luminosity function evolution, and found that while it matched what would be expected from passive evolution up to z ∼ 1.3, it disagreed with such a model at z & 1.3, which they suggested 2 1.2. DO EARLY-TYPE GALAXIES SHARE IN THE WEALTH OF STAR FORMATION ACTIVITY IN HIGH-REDSHIFT CLUSTERS? as evidence for a significant epoch of galaxy assembly via merging (see, however, Andreon 2013; Wylezalek et al. 2014). In the hierarchical evolution framework, massive ETGs are formed as the result of major mergers (Negroponte & White 1983; Barnes 1988; Naab & Burkert 2003; Cox et al. 2006), and these mergers can cause bursts of star formation (Sanders et al. 1988; Barnes & Hernquist 1996; Hopkins et al. 2008) and fuel an active galactic nucleus (AGN Springel et al. 2005) that quenches star formation on the order of a few 100 Myr (Di Matteo et al. 2005; Hopkins et al. 2006). Snyder et al. (2012, hereafter S12) found that red-sequence members have roughly constant stellar ages across 1.0 < z < 1.5—indicating that star formation must be ongoing—and bluer and more stochastic colors at 1.0 < z < 1.3 than would be expected of a passive population. Zeimann et al. (2013), B13, and A14 measured high, and consistent, Hα, 24 µm, and 250 µm star formation rates (SFRs), respectively, in these clusters. With these high SFRs, it is clear that members in 1 < z < 1.5 clusters have not exhausted their supplies of cold gas, which, in combination with the evidence of significant ongoing merger activity, suggests that a substantial number of these mergers are gas-rich. If gas-rich, major mergers are common in clusters then the ETGs formed in these mergers would be expected to have high SFRs, at least for a short time after their formation. Conversely, ETGs observed several hundred Myr post-merger would likely appear to be recently quenched. In Chapter 2, we use high-resolution Hubble Space Telescope (HST ) images of ISCS galaxy clusters at 1 < z < 1.5 to identify isolated, early-type members, and then measure their dust-inferred SFRs using Spitzer 24 µm data. The goal of Chapter 2 is to test whether the high SFRs seen in high-redshift clusters are merely due to the 3 1.3. LOW-REDSHIFT: WHERE HAVE ALL THE STAR FORMERS GONE? morphological mix—a result of the elevated late-type galaxy (LTG) fraction relative to local clusters—or whether significant star formation is present in the early-types as well. 1.3 Low-Redshift: Where Have All The Star Formers Gone? A number of studies in the z . 1 regime show that the star formation of cluster galaxies decreases with age of the Universe (Couch & Sharples 1987; Saintonge et al. 2008; Finn et al. 2008; Webb et al. 2013), while cluster ETG fractions increase (Stanford et al. 1998; Poggianti et al. 2009). However, the majority of studies that investigate cluster star formation tend to look at the gross star formation activity, and do not attempt to quantify it for different morphologies. With the goal of our high-redshift sample in Paper I well established—to quantify the star formation activity of cluster ETGs—and the relative dearth of similar such studies at z < 1, we propose to extend this study of morphology-dependent star formation down to z = 0.15. With cluster ETG fractions increasing as the cluster star formation activity decreases, we expect to find a large population of quenched early-types. What would be surprising is cluster LTG star formation activity at the level of ETGs or lower. In Chapter 3, we describe the publicly available Cluster Lensing and Supernova survey with Hubble (CLASH) data set, and discuss how we calculate stellar masses, and ultraviolet (UV) star formation rates, and how we determine cluster membership, and galaxy morphology. With the overall decline in cluster star formation over the last ∼ 9 billion years, it is clearly necessary to be able to compare cluster galaxies at different times. However, by studying cluster galaxies in the first place, we are taking 4 1.3. LOW-REDSHIFT: WHERE HAVE ALL THE STAR FORMERS GONE? advantage of a population that is (likely) evolving more quickly than galaxies in lowdensity environments, so it would be beneficial to be able to compare galaxies in different environments. To that end, we describe in Chapter 4, a comparison sample of field galaxies drawn from the UltraVISTA survey. We then discuss our empirical SF RUV → SF RIR relations, derived using the UltraVISTA galaxies, in Chapter 5. We use these relations to correct the CLASH UV SFRs to be directly comparable to 24 µm SFRs from ISCS and UltraVISTA. In Chapter 6, we explore galaxy morphologies, mean SFRs, and specific SFRs, first as a function of cluster-centric radius, then as a function of redshift. 5 Chapter 2 Paper I: Star Formation in High-Redshift Cluster Ellipticals 2.1 Paper I Organization Aside from some slight alterations to the sectioning depth, §2.2 to §2.6 are reproduced here as they were submitted in Paper I. As noted above, the majority of Paper I’s introduction section appears in Chapter 1. In §2.2 we summarize the ISCS cluster sample, multi-wavelength data sets, and red-sequence catalogs of S12 which form the basis of this work. In §2.3 we describe our criteria for selecting isolated, early-type cluster members, and present our measurements of their star formation activity in §2.4. We discuss our results in §2.5 and present our conclusions in §2.6. 6 2.2. DATA 2.2 Data 2.2.1 IRAC Shallow Cluster Survey In the ISCS, Eisenhardt et al. (2008) identified candidate galaxy clusters in a 7.25 deg2 area of the Boötes field of the NOAO Deep Wide-Field Survey (NDWFS; Jannuzi & Dey 1999), over a redshift range of 0.1 < z < 2, using imaging from the IRAC Shallow Survey (Eisenhardt et al. 2004). Using accurate photometric redshifts from Brodwin et al. (2006), a wavelet algorithm was used to identify clusters from the 4.5 µmselected galaxies as three-dimensional overdensities. The cluster centers were taken to be the peaks in the wavelet detection maps. Three more epochs were subsequently obtained in all IRAC bands as part of the Spitzer Deep, Wide-Field Survey (SDWFS; Ashby et al. 2009), which increased the photometric depth of the IRAC images by a factor of two. The deeper SDWFS data were used to improve the photometric redshift accuracy for all galaxies, as well as to extend the catalog to lower flux limits. The SDWFS catalog is 80% complete at 18.1 mag in the 4.5µm band (Ashby et al. 2009). In this work we focus on 11 spectroscopically confirmed 1 < z < 1.5 ISCS clusters selected by S12 for follow-up HST observations, as described below. We list these clusters, along with their positions and spectroscopic redshifts, in Table 2.1. 2.2.2 HST Data A subset of ISCS clusters spanning 1 < z < 1.5 were imaged with HST in the NIR and optical with instrument and filter combinations chosen to bracket the 4000 Å break. NIR data were acquired with the Wide Field Camera 3 (WFC3; Kimble et al. 2008) at F160W. In the optical, observations were either taken with the Advanced 7 2.2. DATA Table 2.1: ISCS Clusters ISCS Cluster Name R.A. (J2000) Dec. (J2000) zspec Visually Selected ETGs Visually Selected LTGs J1429.2+3357 J1432.4+3332 J1426.1+3403 J1426.5+3339 J1434.5+3427 J1429.3+3437 J1432.6+3436 J1433.8+3325 J1434.7+3519 J1438.1+3414 J1432.4+3250 14:29:15.16 14:32:29.18 14:26:09.51 14:26:30.42 14:34:30.44 14:29:18.51 14:32:38.38 14:33:51.13 14:34:46.33 14:38:08.71 14:32:24.16 33:57:08.5 33:32:36.0 34:03:41.1 33:39:33.2 34:27:12.3 34:37:25.8 34:36:49.0 33:25:51.1 35:19:33.5 34:14:19.2 32:50:03.7 1.059 1.112 1.136 1.163 1.238 1.262 1.349 1.369 1.372 1.414 1.487 8 3 5 8 2 6 3 5 1 4 4 5 4 7 5 4 3 3 7 7 7 6 Total Number 49 58 Camera for Surveys (ACS; Ford et al. 1998) in filters F775W, F814W or F850LP, or with the Wide Field Planetary Camera 2 (WFPC2; Holtzman et al. 1995) at F814W. The reader is referred to S12 for a more detailed description of these data. We use the all-HST color-magnitude relations (CMRs) of 11 distant, spectroscopically confirmed ISCS clusters presented in S12. That work isolated the CMRs by subtracting a passively evolving Coma CMR model with a fixed rest-frame slope. Galaxies brighter than an evolving magnitude limit of H ∗ (z) + 1.5 with color offsets, ∆, within −0.25 < ∆ < 0.75 of the model CMR were identified as red-sequence galaxies. To reduce the effect of outliers, they removed galaxies that were more than two median absolute deviations in color from their measured ∆ zero point. We independently calculate the F160W completeness via Monte Carlo methods. We insert 100 artificial stars into each WFC3 cluster image in steps of 0.05 magnitudes from 23 to 25.5, ensuring a minimum separation of 5 pixels between input stars. We 8 2.2. DATA repeat this procedure 10 times providing 1000 input sources per magnitude bin, per cluster. We run SExtractor (ver. 2.8.6, Bertin & Arnouts 1996) on each image to generate source positions and mag auto magnitudes. We match the SExtractor positions with our input positions in each magnitude bin, counting the source as matched if it lies within 2 pixels of the input position of the artificial star. For each matched source, we calculate the magnitude difference between the input source and mag auto output, and find that the absolute mean ∆mag is . 0.2 up to a magnitude of 24.9. We perform a least squares fit to the fraction of matched sources and find a 90% completeness limit of 24.1 mag, consistent with that found in S12. We confirm that all galaxies in our final cluster sample (§2.3) have an F160W magnitude brighter than our 90% completeness. 2.2.3 Mid-Infrared Data The high-redshift clusters studied in this work were also imaged at 24 µm with the Multiband Imaging Photometer for Spitzer (MIPS; Rieke et al. 2004). The exposure times, which increased with redshift from 12 to 48 min, were designed to produce similar sensitivities in IR luminosity for all clusters. Following the method of Magnelli et al. (2009), MIPS source catalogs were generated by using the positions of objects in the higher-resolution IRAC images as priors. This method produces 24 µm flux measurements (or limits) for all IRAC galaxies. For consistency with B13, we infer total IR luminosities for these sources using templates from Chary & Elbaz (2001), and convert these to SFRs using the Murphy et al. (2011) relation. The 1 σ depth of our SFRs is ∼ 13 M yr−1 . While the Chary & Elbaz (2001) templates typically overestimate LIR at z > 1.5 9 2.3. GALAXY SELECTION METHOD by a factor of ∼2–8 (Murphy et al. 2009; Nordon et al. 2010; Rodighiero et al. 2010), it provides an estimate that is accurate to 40% up to z ∼ 1.5 (Marcillac et al. 2006; Murphy et al. 2009; Elbaz et al. 2010). In determining their total LIR to SFR calibration, Murphy et al. (2011) assumed that the entire Balmer continuum is absorbed and reradiated by optically thin dust. They also assumed a solar metallicity, and continuous star formation over a timescale of ∼100 Myr. The relation was defined using a Kroupa (2001) IMF, which has a similar normalization to the Chabrier (2003) IMF we use to calculate our stellar masses. 2.2.4 Chandra X-ray Data Murray et al. (2005) obtained X-ray imaging of the NDWFS field with Chandra to depths of 5–15 ks. Follow-up Cycle 10 Chandra observations brought the exposure time to a uniform depth of 40 ks for the clusters in the present sample. 2.3 Galaxy Selection Method We first build our cluster member sample by selecting galaxies that are robust spectroscopic or red-sequence members (§2.3.1). We remove galaxies that likely harbor AGNs (§2.3.2), and galaxies that fall below our uniform stellar mass cut (§2.3.3). In order to robustly measure SFRs for cluster members, we select galaxies that are free from potential 24 µm contamination due to nearby neighbors, based on visual inspection of optical and IR images (§2.3.4). We then visually classify our isolated cluster members, separating them into ETGs and LTGs (§2.3.5). Finally, we describe our selection of a comparison sample of high-redshift field galaxies in §2.3.6. 10 2.3. GALAXY SELECTION METHOD 2.3.1 Identification of Cluster Members We select the 996 galaxies from the 11 high-redshift ISCS clusters studied in S12 for which we have optical/NIR (HST ) and 24 µm (Spitzer) images. We match these galaxies to the 4590 SDWFS catalog positions for which we have SFR (§2.2.3), and stellar mass (§2.3.3) estimates. We choose galaxies where the separation between the Spitzer and HST positions is ≤ 200 . The matched sample consists of 465 galaxies. The F160W-selected galaxies without matches are undetected in the SDWFS IRAC imaging, and hence they fall below the uniform IRAC-based stellar mass cut we impose below. To identify the subset of galaxies that are robust cluster members, we only retain for the final catalog objects with either high-quality spectroscopic redshifts consistent with membership (Eisenhardt et al. 2008; B13; Zeimann et al. 2013), or those that are red-sequence members based on HST photometry, as described in §2.2.2. We cut 219 non-members, reducing our sample to 246 galaxies. 2.3.2 Rejection of AGNs The presence of an AGN can affect the mid-infrared (MIR) flux, potentially leading to an incorrect estimate of the SFR. While only 1% of local cluster galaxies show AGN signatures (Dressler et al. 1985), the surface density of AGNs increases with redshift (Galametz et al. 2009; Martini et al. 2013), making the effect more prominent in our redshift range of interest. Following B13, we remove AGNs identified via either X-ray or MIR techniques. Galaxies whose counterparts in our 40 ks Chandra images were point sources with hard X-ray luminosities brighter than LX,H > 1043 erg s−1 were removed as likely 11 2.3. GALAXY SELECTION METHOD AGN. Similarly, objects with signal-to-noise (S/N) ≥ 5 in all IRAC bands that fall in the AGN wedge from Stern et al. (2005), which are reddened in the MIR due to heating of their dust by AGN, were also removed. These cuts removed a total 12 objects (∼5% of cluster members), bringing our sample to 234 position-matched cluster members apparently free of significant AGN contamination. The resulting SFRs will necessarily be lower limits due to these exclusions. 2.3.3 Stellar Masses and Mass Limit We measure stellar masses for our galaxies using the Bayesian spectral energy distribution (SED) fitting code, iSEDfit (Moustakas et al. 2013), which infers galaxies’ physical properties by fitting population synthesis models to their broadband spectral energy distributions. In this work, we use population synthesis models from Bruzual & Charlot (2003), which are based on the Padova 1994 stellar evolutionary tracks (Girardi et al. 1996), the stelib empirical stellar library (Le Borgne et al. 2003), and the Chabrier (2003) IMF. While all of the cluster members, and indeed all the galaxies from our initial sample, are brighter than our 90% F160W completeness limit of 24.1 mag, we impose the 80% IRAC-based completeness limit from B13. We remove the four galaxies that fall below this limit, resulting in 230 cluster members with log(M? /M ) > 10. 2.3.4 Isolation Because of its broad point spread function (∼600 , Rieke et al. 2004), a single source in the 24 µm band of MIPS can be comprised of multiple distinct physical sources. Due to the difficulty in deconvolving a multiple-object SFR into its constituent SFRs, we 12 2.3. GALAXY SELECTION METHOD choose to limit our work to isolated objects. To that end, we visually inspect 24 µm Spitzer images and the available optical HST images of the remaining 230 cluster members, removing from our final sample those for which we are not able to rule out significant contributions to the MIPS flux from other nearby objects. Through these inspections, we further reduce our sample by 123 galaxies, resulting in 107 isolated cluster members with no NIR or X-ray luminous AGNs. This cut on isolation removes interacting and merging members, thus likely lowering our total measured SFRs. It was necessary, however, to obtain robust SFR measurements for morphological early-types, a key goal of this work. The significant star formation activity seen amongst the isolated red-sequence galaxies (§2.4) is therefore a lower limit. We do not attempt to correct the SFR to the total value, but simply note that the sense of the correction—to higher cluster SFRs for both earlyand late-type members—serves to strengthen our conclusions. 2.3.5 Morphology We visually inspect the optical and NIR HST images of the 107 galaxies in our sample, classifying those consistent with smooth elliptical and S0 shapes as ETGs. Galaxies which exhibit either late-type signatures, or disturbed or irregular morphologies, are collectively classified as LTGs. By requiring the ETGs to have smooth early-type profiles with no signs of interaction, we are removing from this sample galaxies with merger signatures, which again biases our sample against ETGs with potentially higher SFRs. S12 performed visual inspections of their sample using F160W images, assigning morphologies and recording the local environments. Additionally, Sérsic indices (ns ) 13 2.3. GALAXY SELECTION METHOD Figure 2.1: 500 ×500 cutouts of five isolated cluster ETGs, with HST filter listed on each image. Listed below each pair of images is the galaxy name, spectroscopic cluster redshift, SFR, and stellar mass. 14 2.3. GALAXY SELECTION METHOD were measured in the F160W filter for all S12 galaxies using Galfit (Peng et al. 2010) and Galapagos (Häußler et al. 2011), with ETGs defined as having ns > 2.5. These Sérsic index measurements will be described in more detail by C. Mancone et al. (2014, in preparation). As a test of the robustness of our visual morphological classifications, we compare with the independent S12 classifications. The primary difference between these two morphological catalogs is that our classification takes advantage of the higher resolution ACS and WFPC2 HST images, in addition to using the F160W images. After applying isolation and AGN cuts to the visually classified S12 sample consistent with those in the present work, we find agreement for ∼85% of the ETG sample. Where the visual morphologies differed, the cause tended to be late-type features (typically disks with spiral structure) that were clearly visible in the high resolution optical images but not apparent at F160W. We also test the Sérsic indices measured from the F160W images, finding that this quantitative measure disagrees with ∼30% of our visual classifications. This high discrepancy level is in line with the 30-40% sample contamination reported by Mei et al. (2012) for selecting morphology using Sérsic indices. For this work we choose to use visual morphologies, though we have run the analysis both ways and have verified that none of the major qualitative results depend on this choice. Our final sample of visually classified ETGs (LTGs) contains 49 (58) galaxies, and the breakdown by cluster is listed in Table 2.1. In Figure 2.1, we show 500 ×500 optical (left) and NIR (right) cutouts of isolated cluster ETGs of varying SFR and redshift. Below each galaxy, we list its name, spectroscopic cluster redshift, SFR, and stellar mass. At these redshifts, 500 corresponds to ∼41–43 kpc. 15 2.3. GALAXY SELECTION METHOD 2.3.6 Comparison Field Sample Selection We select our comparison sample of 1.0 < z < 1.5 field galaxies from UltraVISTA (McCracken et al. 2012; Muzzin et al. 2013), a deep Ks -selected survey covering 1.62 deg2 of the COSMOS field (Scoville et al. 2007). The publicly available UltraVISTA survey1 has photometry in 30 bands, used to calculate photometric redshifts and infer stellar masses, and also includes Spitzer photometry in the 24 µm band of MIPS and all four channels of IRAC (Muzzin et al. 2013). We select only isolated galaxies by removing photometric catalog members with a neighbor within 600 . We also exclude likely AGNs using IRAC photometry to identify galaxies that fall into the Stern et al. (2005) wedge. Muzzin et al. (2013) calculated stellar masses using Bruzual & Charlot (2003) models, and assuming a Chabrier (2003) IMF. We impose on our field sample the z ∼ 1 UltraVISTA 100% mass completeness cut of log(M? /M ) > 9.74. Following the method in §2.2.3, we infer total IR luminosities using the Chary & Elbaz (2001) templates, and calculate SFRs with the Murphy et al. (2011) relation. We use the updated, higher-redshift version of the morphological catalog of Cassata et al. (2007)2 to separate ETGs and LTGs. Our final comparison sample contains 91 ETGs and 2109 LTGs. 1 http://www.strw.leidenuniv.nl/galaxyevolution/ULTRAVISTA/Ultravista/Data_ Products_Download.html 2 Publicly available COSMOS data sets, including the morphological catalog used in this work, are located at http://irsa.ipac.caltech.edu/data/COSMOS/datasets.html 16 2.4. ANALYSIS Figure 2.2: SFR versus cluster-centric radius for cluster ETGs (red filled circles), and LTGs (blue open squares). The horizontal dashed line is the 1 σ SFR detection level (13 M yr−1 ), and the error bar represents the 40% systematic uncertainty in the SFRs. SFRs of isolated cluster galaxies—of all morphological types—are weakly correlated with projected radius. 2.4 Analysis 2.4.1 Star Formation Rate vs. Radius All the galaxies in our sample are robustly detected in optical and IRAC imaging, and our MIPS 24 µm fluxes are measured for all sources using these positional priors. The resulting SFRs are thus physically meaningful down to very low significances, albeit with large uncertainties. In Figure 2.2 we plot SFR versus projected cluster-centric radius for cluster ETGs 17 2.4. ANALYSIS (red filled circles) and LTGs (blue open squares). Galaxies plotted below the horizontal dashed line have SFRs below our 1 σ depth of 13 M yr−1 . The large error bar shows the systematic error in the SFR, which we take to be 40%, based on a comparison between 24 µm and Herschel SFR measurements over z = 0–1.5 (Elbaz et al. 2010). On their own, the ETG and LTG samples show little-to-no radial dependence in the SFRs. However, when considering all cluster galaxies, we do find a weak correlation (Spearman’s rs = 0.30 ± 0.06 at the 99% confidence level) between SFR and cluster-centric radius. As can be seen in Figure 2.2, we are largely limited to radii less than 0.75 Mpc due to the small footprint of WFC3. We are able to probe beyond 1 Mpc in J1432.4+3250 due to two adjacent pointings on this cluster. We have verified our results are unchanged if we limit our analysis to the well-sampled region below 0.75 Mpc. 2.4.2 Mean Star Formation Rate To explore the effect of environment on the SFRs of both ETGs and LTGs, we show in Figure 2.3 the mean SFR, hSF Ri, as a function of projected cluster-centric radius. ETGs are plotted as red circles and LTGs as blue squares. We separate the galaxies into three non-overlapping annuli, selecting the radial bins such that the S/N in each is approximately equal. From inner to outer, the bin sizes are 200, 175, and 800 kpc, respectively. The errors in each bin are calculated from the quadrature sum of bootstrap resampling (1000 samples, with replacement) and simple Poisson errors. We also show the mean SFR of our comparison sample of field ETGs with the red horizontal line. The 68% error in the mean is given as the shaded region. 18 2.4. ANALYSIS Figure 2.3: Mean SFR versus cluster-centric radius for ETGs (red circles) and LTGs (blue squares), with bin widths depicted by the horizontal error bars. In each bin, the median SFR of non-detections is assigned to each undetected galaxy and included in the mean. The vertical error bars show the quadrature sum of bootstrap resampling and Poisson error. The solid horizontal red line (shaded region) is the mean SFR (error) for field ETGs. On average, cluster ETGs are forming stars at a rate almost 15% that of cluster LTGs, and 32% that of field ETGs. In computing the mean SFRs, objects with individual SFRs below 13 M yr−1 were assigned the median value of all such objects in the bin. This is the catalog-space equivalent of median stacking; from inner to outer annuli, the median SFRs of these ETGs (LTGs) are 1.5, 2.6, and 3.0 (3.6, 2.8, and 2.7) M yr−1 . We have verified that none of our main results change even in the extreme case of setting the SFRs of all such < 1 σ SFRs to zero. We find mean SFRs of 5.1 ± 2.4, 5.6 ± 2.1, and 12.0 ± 5.3 M yr−1 for our cluster ETG sample from the inner to outer annuli, respectively. With mean SFRs 19 2.4. ANALYSIS of 41.4 ± 17.3, 49.4 ± 21.1, and 55.2 ± 14.3 M yr−1 over the same range, the LTGs have mean SFRs ∼5 to 9 times higher at all radii. The ETGs and—to a lesser extent—the LTGs show some decrease in mean SFR at small radii, although this is not statistically significant given the large errors. Averaging over all radii we find a mean SFR of 7.4 ± 2.0 M yr−1 for cluster ETGs, which is a factor of 3.1 lower than the mean SFR of field ETGs (hSF Ri = 23.2 ± 5.0 M yr−1 ). While the uncertainty in our SFRs is too large to determine any radial dependence, we do find that cluster ETGs show suppressed star formation activity relative to field ETGs. We note that even with 91 galaxies in the sample, the mean SFR of field ETGs is heavily affected by one galaxy with SFR > 300 M yr−1 . However, the mean SFR without this object (19.6 ± 3.0 M yr−1 ) would still be more than a factor of two higher than that of cluster ETGs. These results show that although cluster ETGs have SFRs that are fairly quenched relative to both their field analogs and the remainder of the cluster population, they still contribute 12.7% of the vigorous star formation observed in these clusters. 2.4.3 Fraction of Star-forming Galaxies In Figure 2.4, we plot fSF , the fraction of star-forming, visually selected cluster ETGs (red circles) as a function of cluster-centric radius. We conservatively limit this measurement to members with SFRs of at least 26 M yr−1 , above our 2 σ detection level. We use only two annuli due to the relatively small size of our sample, and use the binomial error in the fraction as our total error. The radial bins and error ranges are shown by the horizontal and vertical error bars, respectively. The horizontal red line shows the fraction of star-forming field ETGs, with the binomial error in the fraction 20 2.4. ANALYSIS Figure 2.4: Fraction of star-forming galaxies versus projected radius for high-redshift cluster ETGs (red circles) and low-redshift cluster galaxies from C11 (gray squares and error bars). For cluster ETGs, the size of the horizontal error bars represents the bin widths, and the vertical error bars represent the binomial error in our fractions. The lower axis corresponds to the data from this work; the upper from C11. The lower gray curve is a least squares fit to all six points from Figure 4 of C11 (extending to 3Rproj /R200 ), while the upper gray curve is the same fit, shifted up by a factor of 11.6. The red horizontal line (shaded region) shows the fraction (error) for the comparison high-redshift field ETGs. High-redshift cluster ETGs have star-forming fractions at least an order of magnitude higher than local cluster galaxies, and two times lower than field ETGs in the same redshift range. shown by the shaded region. The gray points and error bars show the fraction of local (z . 0.1) star-forming (LIR > 4.7 × 1010 L ) cluster galaxies versus projected radius, from Chung et al. (2011, hereafter C11), who studied 69 low-redshift clusters with 21 2.4. ANALYSIS total dynamical masses in the range ∼ (1 − 7) × 1014 M , determined by using caustic infall patterns (Rines & Diaferio 2006), and selecting only galaxies brighter than Mr = −20.3. The lower x-axis corresponds to the projected cluster-centric radius for this work, while the upper x-axis corresponds to the projected R200 -normalized radius from C11. Based on the X-ray, weak lensing and dynamical masses that have been measured for a subset of the Boötes clusters (Brodwin et al. 2011; Jee et al. 2011), as well as on a clustering analysis of the full ISCS sample (Brodwin et al. 2007), our z > 1 ISCS clusters have halo masses in the range ∼(0.8 − 2) × 1014 M , and virial radii of ∼1 Mpc. Therefore the upper and lower axes in Figure 2.4 are approximately equivalent. While the median mass of the C11 clusters is larger than that of the Boötes clusters, the latter will grow in mass in the ∼8–9 Gyr to the present epoch. +6.6% Averaging over all radii, we find that 12.2%−4.7% of isolated cluster ETGs are star forming. It is clear that the star-forming fraction is substantially higher at z > 1.0 than it is locally. We quantify this difference by first fitting a least squares curve to all six C11 points (while only the first three points are shown in Fig. 2.4, the C11 measurements extend to 3Rproj /R200 ), then using χ2 minimization to determine that a simple scaling factor of 11.6 provides an excellent fit to our data. In both radial bins, we find that the fraction of star-forming ETGs in our sample is approximately an order of magnitude higher than for local cluster galaxies of all types. Due to the very low redshift of the clusters in C11 (z . 0.1), their minimum cutoff for star-forming galaxies (LIR > 4.7 × 1010 L ) is ∼ 3 times lower than our 2 σ level of ∼1.5 × 1011 L . Using the published LIR values from C11, we find that six of the 109 cluster galaxies (within 3Rproj /R200 ) considered star-forming in C11 have LIR > 1.5 × 1011 L , which is only ∼0.1% of their cluster member sample, a factor 22 2.4. ANALYSIS of ∼18 lower than with their SFR cut, and ∼100 times lower than our cluster ETG fraction. Within the errors, we do not have enough evidence to determine whether there is a radial trend in our ETGs, though the shifted radial profile of C11, indicated by the upper curve, is clearly consistent with our data. Also, the fSF of field ETGs (0.28+0.06 −0.05 ) is 2.3 times greater than that of cluster ETGs, implying an environmental dependence on ETG star formation. 2.4.4 Specific Star Formation Rate We next explore the specific star formation rate (sSFR), defined as the sum of the SFRs divided by the sum of the stellar masses, in each radius bin. In normalizing the SFR of a galaxy to its mass, the sSFR allows us to explore the relative efficiency with which it converts its cold gas into stars. In Figure 2.5 we plot the sSFR versus radius for both our cluster ETGs (red circles and error bars) and for the low-redshift, star-forming, cluster galaxies of C11 (gray points). The radial binning and error ranges for our points are calculated as in Figure 2.3. We show the sSFR and similarly calculated error of field ETGs as the red horizontal line and shaded region, respectively. As in §2.4.3, we fit a best-fit curve to the C11 points, then determine the shift in amplitude required to match our cluster ETG points. We find that scaling the curve by a factor of 102.08 , also shown in the figure, provides a good fit to our high-redshift sSFR measurements. High-redshift cluster ETGs are forming stars at a rate 120 times higher than local cluster galaxies. C11 calculated sSFR for star-forming (LIR > 4.7 × 1010 L ) galaxies, while we 23 2.4. ANALYSIS Figure 2.5: Specific SFR versus radius for high-redshift cluster ETGs (red filled circles) and low-redshift cluster galaxies from C11 (gray filled squares), with the same x-axes as Figure 2.4 (§2.4.3). The vertical error bars represent the bootstrapping and Poisson error in our sSFRs, and the horizontal error bars show the size of each bin. The lower gray curve is a least squares fit to the C11 points (all six points from their Fig. 3, extending to 3Rproj /R200 ), while the upper gray curve is the same fit shifted up by a factor of 120. The red horizontal line (shaded region) shows the sSFR (error) for the comparison high-redshift field ETGs. The sSFR of high-redshift cluster ETGs is 19 times lower than similar redshift field ETGs, yet more than two orders of magnitude larger than low-redshift cluster galaxies of all types. While we place no lower limit on the LIR of high-redshift cluster ETGs, C11 only measured sSFR for galaxies with LIR > 4.7 × 1010 L (their star-forming cut; see §2.4.3). The sSFR of C11’s cluster galaxies would be lower if no cut was imposed, making the factor of 120 we find here a lower limit. place no such constraint on either high-redshift ETG sample plotted in Figure 2.5. Additionally, the morphological mix of the low redshift cluster galaxies to which 24 2.5. DISCUSSION we are comparing is unclear. Although clusters in the local Universe are primarily inhabited by early-type, “red and dead” galaxies (Oemler 1974; Dressler 1980), the star-forming subset detected by C11 may be preferentially drawn from the small fraction of late-type members or from recently accreted field galaxies. Correcting for such LTG contamination, and removing the LIR limit, would lower the sSFR in the low redshift sample, and hence make the evolution over this redshift range even more dramatic. Averaging across all radii, the cluster ETGs have an sSFR of 0.11 ± 0.04 Gyr−1 , which is a factor of 19.0 lower than the sSFR of the field ETG sample (2.02 ± 0.42 Gyr−1 ). While we cannot definitively determine whether there is a radial trend in cluster ETG sSFR, this drop relative to ETGs in the field is further evidence for the environmental dependence of the star formation of ETGs, as shown in the previous two sections. What is not immediately clear is how much of this offset between cluster and field ETGs is due to the difference in stellar mass—both the distribution, and minimum cut applied in §2.3.3—and how much is due to the actual SFRs. To quantify this, we find that the mean stellar mass of cluster ETGs is 7.0 × 1010 M , a factor of 6 times larger than the mean stellar mass of field ETGs (1.2×1010 M ). This, however, only accounts for 31% of the offset between the field and cluster, implying that the remaining ∼70% is due to the quenching of cluster ETGs relative to the field. 2.5 Discussion Two related studies (B13, and A14) identified high levels of star formation activity in a superset of the ISCS clusters studied in this work. However, these studies could not 25 2.5. DISCUSSION isolate the ETGs due to a lack of morphological information. In the present work, which benefits from the high-resolution imaging of HST, we are able to expand upon their results by analyzing the star formation properties of isolated, massive ETGs, and comparing them to high-redshift ETGs in the field and to galaxies in low-redshift clusters. 2.5.1 Comparison to Brodwin et al. (2013) B13 measured the star formation activity in 16 ISCS galaxy clusters at 1.0 < z < 1.5, including the 11 clusters studied in this work. Their large sample enabled them to finely bin their data as functions of both redshift and radius. The morphology and isolation cuts in the present work result in a relatively small sample size that precludes a similar analysis, but instead permits an investigation of physically interesting subsamples of these cluster members. Both B13 and this work used the same 24 µm Spitzer photometry, so the measurements of star formation activity should be consistent, despite the sample size difference. To test this, we first compared, as a function of redshift, the fraction of isolated star-forming galaxies of all types (by combining ETGs and LTGs) from this work to the fraction of star-forming galaxies from B13 within 1 Mpc. We used their redshift binning (1 < z < 1.2, 1.2 < z < 1.37, and 1.37 < z < 1.5) and adopted their (S/N ≥ 4) flux limit, corresponding to SFR & 47 M yr−1 . Our results are in good agreement with those reported in B13. Probing to lower star formation rates, we plot in Figure 2.6 the fraction of isolated star-forming cluster galaxies down to our full 2 σ SFR ≥ 26 M yr−1 limit. We bin the galaxies (1 < z < 1.16, 1.16 < z < 1.35, and 1.35 < z < 1.5; shown by the horizontal 26 2.5. DISCUSSION Figure 2.6: Fraction of isolated, high-redshift star-forming (SFR ≥ 26 M yr−1 ) cluster galaxies (see legend for symbol types) as a function of redshift. Vertical error bars show the binomial error in the fractions, while the horizontal error bars show the redshift binning for all three sets of cluster galaxies. The significant drop in star-forming fraction from 61.0% to 26.5% exhibited by all cluster galaxies (green triangles) at z ∼ 1.4 is in good agreement with the redshift of transition—away from vigorous star formation—observed by B13. error bars) such that the binomial error in each redshift bin is approximately equal for galaxies of all morphological type (green filled triangles). ETGs are plotted as red circles and LTGs as blue squares, slightly offset to the left and right, respectively. 61.0% of all isolated cluster galaxies are star-forming at 1.35 < z < 1.5, followed by a decrease to 26.5% in the middle redshift bin. This result is in good agreement with the transition redshift of z ∼ 1.4 found by B13 between the era of vigorous star formation in high-redshift clusters and the quenched epoch at later times. The LTG population experiences a similar trend, with a decrease from 77.8% to 46.7%, 27 2.5. DISCUSSION over z ∼ 1.4 → 1.25, while the ETG sample shows a milder downward trend in starforming fraction at z ∼ 1.4 (from 28.6% to 10.5%), though it is formally consistent with being constant within the errors. 2.5.2 Comparison to Alberts et al. (2014) A14 explored 274 ISCS clusters from z = 0.3 to 1.5, including ∼100 over the same redshift range as our observations. By stacking 250 µm Herschel data, they were able to probe to mean LIR values almost an order of magnitude lower than our 1 σ detection limit. Although our sample is a subset of the A14 sample, the measurement techniques—24 µm detections versus stacking at 250 µm—are relatively independent. Here we compare some of our results with those reported in A14. In order to compare the two SFR measurements for our morphologically selected sample, we first attempted to directly measure stacked 250 µm fluxes for our ETG and LTG samples. However, with the relatively small sample size, and source contamination due to the large beam size (18.00 1, Swinyard et al. 2010), the S/N was too low to permit this measurement. In Figure 2.7 we compare mean SFR as a function of redshift, derived from 24 µm and 250 µm measurements. To be consistent with the radial selection in A14, we only plot galaxies with cluster-centric radii < 1 Mpc. We plot our cluster galaxies as the filled points, using the same binning as in Figure 2.6. The filled green triangles represent our isolated cluster galaxies of all morphologies, while the open triangles show the mean SFR of galaxies of all types from A14. Despite the order of magnitude difference in observed wavelength, and the very different measurement methodologies, the results are in excellent agreement for galaxies of all morphological types. 28 2.5. DISCUSSION Figure 2.7: Mean SFR versus redshift for our isolated cluster galaxies (filled symbols, with the same binning as in Figure 2.6) and for cluster galaxies from A14 (open symbols), with all galaxies having cluster-centric radius < 1 Mpc. Our errors are calculated as the quadrature sum of bootstrapping and simple Poisson errors. The two different measurement methods used for calculating these SFRs—24 µm detections versus stacking at 250 µm—show consistent results. While unable to visually or quantitatively determine morphologies, A14 matched each galaxy in their sample against seven Polletta et al. (2007) templates representing different morphologies. Using this template fitting as a proxy for color, A14 selected galaxies that were best fit by late-type templates as “blue” (star-forming) galaxies. We plot the mean SFR of these galaxies as the light blue open squares, and compare them to our LTGs (blue filled squares). We again find that there is excellent agreement between the 250 µm-derived SFRs in A14 and the 24 µm-derived SFRs in this work. 29 2.5. DISCUSSION 2.5.3 Star Formation in High-Redshift Cluster ETGs Low-redshift ETGs, particularly those in clusters, have quiescent, old stellar populations. A key issue in the evolution of galaxy populations in clusters is determining the nature of the star formation history of these “red-and-dead” galaxies. Specifically, when did present-day massive cluster ETGs experience their last major burst of star formation? Determining the epoch during which these galaxies experienced such a burst can provide constraints on when cluster galaxies experienced their last phase of gas-rich major merging. We find that the fraction of star-forming galaxies is an order of magnitude larger for our 1 < z < 1.5 cluster ETGs than for local cluster galaxies of all morphologies (C11). Measurements of star-forming fractions in nearby clusters, such as those by C11, necessarily include large contributions from late-type galaxies as ETGs typically have SFRs below the survey limits. The increase we find is therefore a lower limit to the evolution between nearby and z > 1 cluster ETGs. Even more striking is the comparison of the mass-normalized star formation rates between these two galaxy populations. With sSFRs more than two orders of magnitude higher than cluster galaxies in the local universe, our high-redshift cluster ETGs have significantly more ongoing star formation activity per unit stellar mass. This dramatic evolution would be even more extreme if C11 had measured the sSFRs of all galaxies above a fixed mass limit, rather than just those above their star formation detection limit. An interesting result stemming from the comparisons in §2.5.1 and §2.5.2 is that despite the decline in the fraction of star-forming (SFR ≥ 26 M yr−1 ) cluster ETGs 30 2.5. DISCUSSION at z ∼ 1.4 seen in Figure 2.6, this population has mean SFRs that are roughly constant across this period. Specifically, although we see that the star-forming fraction drops ∼18 percentage points (albeit with very large scatter) at this redshift, their mean SFRs (shown in Figure 2.7) remain relatively constant from z ∼ 1.4 → 1.25. One potential conclusion from this is that while a significant quantity of early-type galaxies are being quenched, there must be some mechanism that is enhancing the star formation activity of the remaining star-forming ETGs. If the above results are not a product of environment, we would expect to see ETGs in both the cluster and the field have a similar lack of SFR evolution. To determine whether this is the case, we plot in the upper panel of Figure 2.8 the sSFR for cluster (red filled circles) and field (pink open circles) ETGs, and field LTGs (light blue open squares) as a function of redshift. We choose to plot sSFR as it best represents the efficiency of star formation activity. For our cluster ETGs, we use the same binning as in Figures 2.6 and 2.7, while we bin the field galaxies such that the S/N of the ETGs is approximately equal in each bin. Errors are calculated as the quadrature sum of bootstrap resampling and simple Poisson errors. Not only do field ETGs show a steady decreasing trend in sSFR from z = 1.5 → 1, but also their sSFRs drop by a factor of 4.5 over this redshift range, in line with the factor of 4.0 decrease we find for field LTGs. Cluster ETGs, on the other hand, show little-to-no evolution between the two highest redshift bins, followed by a factor of 3.5 decrease at z ∼ 1.08, however, with a very large scatter. The lack of evolution in the cluster ETG sSFR between the two highest redshift bins may suggest that any mechanism operating to sustain their SFRs is dependent on environment. In the lower panel of Figure 2.8 we plot the fraction of isolated cluster galaxies 31 2.5. DISCUSSION Figure 2.8: Upper panel: Specific SFR as a function of redshift for ETGs in clusters (red filled circles) and in the field (pink open circles), and for field LTGs (light blue open squares). Errors are calculated as the quadrature sum of bootstrap resampling and simple Poisson errors, as in §2.4.4. Field galaxies show similar sSFR evolution over our redshift range, with ETGs (LTGs) dropping by a factor of 4.5 (4.0). Cluster ETGs show little-to-no evolution at z ∼ 1.4, and are quenched relative to their field counterparts at all epochs. Lower panel: Fraction of cluster galaxies that are ETGs as a function of redshift. that we classified as ETGs, using the same redshift bins as above. We find that the fraction of ETGs increases from 34% to 56% from z ∼ 1.4 → 1.25, which suggests that new ETGs are being formed during this period. 32 2.5. DISCUSSION It should be noted that there are processes other than major merging that may potentially play a role in forming new ETGs, or shaping existing early-types (e.g., Kaviraj et al. 2013). Violent disk instability can cause late-type systems to lose their disks through turbulence, forming compact gas-rich “blue nuggets” with gas inflows similar to those generated by wet mergers (Dekel & Burkert 2014). However, the cold streams (Kereš et al. 2005; Dekel & Birnboim 2006) that feed disks in this model are only important in regions with low galaxy density (Kereš et al. 2005), and likely not a significant factor in the hot halo environment of ISCS clusters. Strazzullo et al. (2010) found a large fraction of quenched, compact ETGs in the z = 1.39 cluster XMMU J2235, suggesting that minor—and likely dry—mergers can increase the size of such galaxies over later epochs, without drastically altering their star formation activity. However, XMMU J2235 is a very massive cluster (∼7 × 1014 M , Jee et al. 2009; Rosati et al. 2009), where major merger activity has likely ceased, and a factor of at least a few times more massive than the clusters studied in this work. As such, the mechanism suggested by Strazzullo et al. (2010) is not likely currently playing a role in ISCS clusters—especially at z & 1.16—when considering the star formation activity shown above. From z ∼ 1.4 → 1.25, the fraction of cluster ETGs that are star-forming drops from 28.6% to 10.5%. It is likely that a substantial number of cluster ETGs formed before z = 1.5, and that they make up a significant portion of this large subset of quenched ETGs that we find in ISCS clusters. However, from 1.35 < z < 1.5 to 1.16 < z < 1.35, there is a 21.8 percentage point increase in the fraction of cluster galaxies that are morphologically early-type; some portion of the remaining star-forming ETGs in this epoch are likely recent byproducts of major mergers. Furthermore, cluster ETG 33 2.6. CONCLUSIONS mean and specific SFRs are roughly constant over this period. These ETGs have not yet had sufficient time for their star formation to be quenched, implying that their progenitors’ mergers occurred relatively recently. Moving into the lower redshift bin (1 < z < 1.16), the star-forming ETG fraction falls to 0%, and their sSFR and mean SFR are both quenched (by factors of 3.5 and 4.0, respectively), while the ETG fraction remains relatively constant. Given our assumptions above about gas-rich major mergers, the dearth of star-forming galaxies, and overall lack of star-formation activity seen in this epoch, at 1 < z < 1.16, suggests we are seeing the quenching of ETGs due to post-merger AGN activity. 2.6 Conclusions We have used a sample of 11 high-redshift (1.0 < z < 1.5), IR-selected ISCS galaxy clusters to investigate the star formation properties of isolated, early-type galaxies. After conservatively removing AGNs through X-ray and IR criteria, we visually inspected our sample using high-resolution HST imaging, separating our galaxy sample into two coarse morphological bins: ETGs and LTGs. We used deep 24 µm imaging from Spitzer to measure the obscured SFRs, excluding galaxies for which we could not rule out contamination from nearby neighbors. We compared the star formation of the cluster ETG sample with low-redshift cluster galaxies, finding an order of magnitude larger fraction of star-forming galaxies, and a greater than two order of magnitude larger sSFR for our high-redshift cluster ETGs. Averaging across our entire cluster ETG sample, we find that 12.2% are still experiencing relatively enhanced (SFR > 26 M yr−1 ) star-formation activity. By comparing the mean SFR of ETGs with LTGs in ISCS clusters, we found that 34 2.6. CONCLUSIONS despite their enhanced star formation relative to low-redshift cluster galaxies, highredshift cluster early-types have substantially less star formation activity relative to the rest of the isolated cluster population. However, averaging across all cluster radii, ETGs still contribute 12.7% of the significant star formation activity observed in these clusters. Due to our relatively small sample size, we were unable to detect the radial dependence in star formation activity reported by B13. However, we found that our cluster ETGs are quenched relative to a comparison sample of field ETGs, by a factor of 3.1 in mean SFR, and 19.0 in sSFR. Even when “correcting” the sSFR of cluster ETGs for their factor 6 higher mean stellar masses, we find that it only accounts for ∼30% of the difference between the field ETG sSFR and the significantly quenched cluster ETG sSFR. We then used the conservative IR luminosity cut from B13 to compare the fraction of star-forming galaxies, fSF , with their results, finding that our measurements in isolated galaxies agreed with the B13 measurements of all cluster galaxies in these z > 1 clusters. We also found that our mean SFR measurements correlated well with those of A14, who measured SFR using stacked 250 µm Herschel flux. We used our 2 σ SFR detection limit (26 M yr−1 ) to explore the fSF evolution from z ∼ 1.5 → 1.0. We considered cluster galaxies of all morphologies and found that while 61.0% are star-forming at z ∼ 1.4, the fraction drops to 26.5% by z ∼ 1.25. This drop of almost 35 percentage points suggests that the epoch of enhanced star formation in these clusters is ending around z ∼ 1.4, a finding consistent with that first reported by B13. Only 28.6% of our ETGs are star forming at 1.35 < z < 1.5, and the fraction drops 35 2.6. CONCLUSIONS to 10.5% by 1.16 < z < 1.35. While the fraction of star-forming cluster ETGs drops 18.1 percentage points over z ∼ 1.4 → 1.25, their mean and specific SFRs are largely unchanged over this period. With a corresponding increase of 21.8 percentage points in the fraction of ETGs over this period, these results are consistent with a scenario where major gas-rich mergers form new early-type galaxies, temporarily enhancing their star-formation activity. A number of recent studies of the ISCS cluster population have presented lines of evidence supporting the role of mergers in building the stellar mass in these clusters. Specifically, the NIR luminosity function evolution disagrees with a passive evolution model at z & 1.3 (Mancone et al. 2010, 2012), galaxies are experiencing substantial star formation (A14, B13, Zeimann et al. 2013), young galaxies are continuously migrating on the cluster red sequence (S12), and an increase in AGN activity has been observed (Galametz et al. 2009; Martini et al. 2013). This work helps to solidify the implications of all these studies by showing that massive ETGs in clusters likely formed in gas-rich mergers. 36 Chapter 3 Cluster Galaxy Data and Sample Selection In this section we will briefly review the data and sample selection methodology for our high-redshift cluster sample, which we introduced in Chapter 2. We will then discuss the low-redshift CLASH cluster observations and sample construction. 3.1 High-Redshift ISCS Clusters Our high-redshift cluster sample is based the IRAC Shallow Cluster Survey (ISCS; Eisenhardt et al. 2008), which identified potential 0.1 < z < 2 galaxy clusters in 7.25 deg2 of the Boötes field of the NOAO Deep Wide-Field Survey (Jannuzi & Dey 1999). A wavelet algorithm was used, with accurate photometric redshifts from Brodwin et al. (2006), to identify three-dimensional overdensities of 4.5 µm-selected galaxies, where the cluster centers were found from the peaks in the wavelet detection maps. We use 11 spectroscopically confirmed clusters over 1 < z < 1.5 selected by Snyder 37 3.2. LOW-REDSHIFT CLASH CLUSTERS et al. (2012) for follow-up HST observations, and studied by Wagner et al. (2014, Chapter 2 of this thesis). These clusters, their positions, and their spectroscopic redshifts, are listed in Table 2.1. IRAC observations were used as positional priors to match the MIPS fluxes to HST catalog sources. We used the Chary & Elbaz (2001) templates to convert the measured 24 µm fluxes to total IR luminosities, then used the Murphy et al. (2011) relation LIR SF RIR = 3.88 × 10−44 −1 M yr erg s−1 ! (3.1) to calculate SFRs. We removed likely AGNs by identifying high-luminosity X-ray point sources, or based on IRAC colors from Stern et al. (2005). Stellar masses were estimated by fitting galaxies SEDs to population synthesis models using iSEDfit (Moustakas et al. 2013), using Bruzual & Charlot (2003) models, and the Chabrier (2003) IMF. We impose the uniform 80% mass completeness limit of log (M? /M ) > 10. We determined cluster membership by spectroscopic redshift if available; if not, we used red sequence membership as determine by Snyder et al. (2012). Finally, we visually classified our high-redshift cluster members to determine both isolation and morphology. Galaxies with nearby neighbors were flagged and removed from the final sample, as we could not reliably measure their SFRs. 3.2 Low-Redshift CLASH Clusters For our low-redshift (0.15 < z < 1) cluster sample, we use the publicly available1 CLASH survey (Postman et al. 2012), which has observations of 25 clusters in 16 1 https://archive.stsci.edu/prepds/clash/ 38 3.2. LOW-REDSHIFT CLASH CLUSTERS HST bands. For each CLASH cluster, the available dataset includes equatorial coordinates, SExtractor (Bertin & Arnouts 1996) stellarity, AB magnitudes in 16 HST filters, and photometric redshift estimates. In Table 3.1, we list the CLASH clusters, their central coordinates derived from X-ray images, and their spectroscopic redshifts (Postman et al. 2012). From our final sample, we remove objects that are likely stars by cutting on SExtractor’s stellarity, only taking objects with stellarity ≤ 0.05. 3.2.1 Redshifts From the literature, we have spectroscopic redshift measurements for five of the 25 CLASH clusters. Specifically, we take redshifts for Abell 383 from Geller et al. (2014), Abell 611 from Lemze et al. (2013), and for Abell 1423, Abell 2261, and RXJ2129 from Rines et al. (2013). Our photometric redshift estimates were derived by Postman et al. (2012) using BPZ (Benítez 2000; Benítez et al. 2004; Coe et al. 2006), a χ2 minimization template fitting software package. 3.2.2 Stellar Masses We estimate stellar masses using an i-band mass-to-light (M/L) ratio, derived using the color-M/L relation (CMLR) log (M? /Li ) = 1.032 × (g − i) − 0.963, 39 (3.2) 3.2. LOW-REDSHIFT CLASH CLUSTERS Cluster Name Abell 383 Abell 209 Abell 1423 Abell 2261 RXJ2129.7+0005 Abell 611 MS2137-2353 RXJ2248.7-4431 MACS1931.8-2635 MACS1115.9-0129 RXJ1532.9+3021 MACS1720.3+3536 MACS0416.1-2403 MACS0429.6-0253 MACS1206.2-0847 MACS0329.7-0211 RXJ1347.5-1145 MACS1311.0+0310 MACS1149.6+2223 MACS1423.8+2404 MACS0717.5+3745 MACS2129.4-0741 MACS0647.8+7015 MACS0744.9+3927 CLJ1226.9+3332 Table 3.1: R.A. (J2000) 02:48:03.36 01:31:52.57 11:57:17.26 17:22:27.25 21:29:39.94 08:00:56.83 21:40:15.18 22:48:44.29 19:31:49.66 11:15:52.05 15:32:53.78 17:20:16.95 04:16:09.39 04:29:36.00 12:06:12.28 03:29:41.68 13:47:30.59 13:11:01.67 11:49:35.86 14:23:47.76 07:17:31.65 21:29:25.32 06:47:50.03 07:44:52.80 12:26:58.37 CLASH Clusters Dec. zspec (J2000) -03:31:44.7 0.189 -13:36:38.8 0.209 +33:36:37.4 0.214 +32:07:58.6 0.224 +00:05:18.8 0.234 +36:03:24.1 0.288 -23:39:40.7 0.315 -44:31:48.4 0.348 -26:34:34.0 0.352 +01:29:56.6 0.353 +30:20:58.7 0.363 +35:36:23.6 0.391 -24:04:03.9 0.396 -02:53:09.6 0.399 -08:48:02.4 0.440 -02:11:47.7 0.450 -11:45:10.1 0.451 -03:10:39.5 0.494 +22:23:55.0 0.544 +24:04:40.5 0.545 +37:45:18.5 0.548 -07:41:26.1 0.570 +70:14:49.7 0.591 +39:27:24.4 0.686 +33:32:47.4 0.890 Restframe g i F475W F850LP F475W F850LP F625W F850LP F625W F850LP F625W F850LP F606W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F625W F105W F775W F105W F775W F125W F775W F125W F775W F125W F775W F125W F775W F125W F814W F125W F105W F140W from Zibetti et al. (2009). The Zibetti et al. (2009) CMLRs were derived using a Chabrier (2003) initial mass function, and exponential star formation history models with starbursts, using revised 2007 versions of the Bruzual & Charlot (2003) stellar population synthesis models; these models were updated to include the thermally pulsing asymptotic giant branch (TP-AGB) stellar evolutionary phase based on isochrones from Marigo & Girardi (2007) and Marigo et al. (2008). 40 3.2. LOW-REDSHIFT CLASH CLUSTERS Figure 3.1: 1250–2500 Å UV window (vertical purple lines) from Kennicutt (1998), and filter response curves for g and i band (black curves), shifted to the cluster redshift listed in each panel. The best-fit HST filters are shown at their observed frame wavelengths. In order to calculate the (g − i) color and i-band luminosity required in Equation 3.2, we first determine, for each cluster’s redshift, the HST filters that best match restframe g- and i-bands. In Figure 3.1, we show two examples of our filter choices, for clusters RXJ2248.7-4431 and MACS0744.9+3927, at redshifts of z = 0.348 and z = 0.686, respectively. We redshift the g and i filter curves to each cluster’s redshift, then select by eye the observed frame HST filters that best match the shifted filters. 41 3.2. LOW-REDSHIFT CLASH CLUSTERS We list these best matches2 in Table 3.1. We calculate, for each CLASH object, its restframe (g − i) color, and M/L ratio. To calculate an object’s stellar mass, we must turn its i-band magnitude into a luminosity. To do this, we use the relation L = 4π [D (z)]2 10(−(m+48.6)/2.5) × f req L (3.3) where m is the magnitude, f req is the frequency of the observed filter in Hz, and D (z) is the luminosity distance of the cluster redshift, which we calculate using the analytical approximation of Wickramasinghe & Ukwatta (2010). Finally, we estimate each object’s stellar mass using its i-band M/L ratio and i-band luminosity. This results in stellar mass estimates for 42483 CLASH objects. 3.2.3 Star Formation Rates For each cluster’s redshift, we determine which WFC3 broadband filter best respresents the restframe UV, which we take to be the 1250–2500 Å UV window suggested by Kennicutt (1998). Similar to the method in §3.2.2 for selecting the best HST filters for g and i band, we shift the UV window to each cluster’s redshift, and determine by eye which filter is best contained within the window. In Figure 3.1, we show the redshifted UV window, and the HST filter that fits best, at the redshift of the cluster listed in each panel. For MACS0744.9+3927 (z = 0.686) and CLJ1226.9+3332 (z = 0.89) we select F275W as our UV proxy. For the remaining 23 clusters, we use F225W. 2 Although F625W is the best g-band proxy for Abell 611, there are no observations in that filter for this cluster, and we use F606W instead. 42 3.2. LOW-REDSHIFT CLASH CLUSTERS Using Equation 3.3, we calculate UV luminosities, converting from solar units to erg s−1 , then use the relation SF RUV L = 4.42 × 10−44 −1 M yr erg s−1 ! (3.4) from Murphy et al. (2011) to calculate star formation rates. This results in SFRs for 29876 CLASH objects. 3.2.4 Cluster Membership For the clusters listed in §3.2.1, for which we have spectroscopic redshifts—except for Abell 611—we select cluster members using the membership flag in the published galaxy redshift tables. For Abell 611, no membership flag exists, so we select as members galaxies with |vcluster − vgalaxy | ≤ 4000 km s−1 , following the velocity cut in Lemze et al. (2013). These selections result in 121 spectroscopic members across the five CLASH clusters. However, we note that our final sample has fewer than this number due to the filter detection requirements in §3.2.2 and §3.2.3. As all 25 CLASH clusters have photometric redshifts estimates, we use these to select the remainder of our z < 1 cluster member sample. To better constrain the accuracy of our photometric redshift-based membership selection, we first use the five CLASH clusters with spectroscopic redshifts to determine how conservative we must be in our photometric selection. Each galaxy’s photometric redshift is published with P (z), an odds parameter, defined as the probability that zphot is contained within 2 × 0.02 (1 + z) (Benítez et al. 2004). Our final sample only contains galaxies with P (z) > 0.9, which are considered the most reliable (Postman et al. 2012). 43 3.2. LOW-REDSHIFT CLASH CLUSTERS Figure 3.2: Distribution of spectroscopically confirmed galaxies based on the total number of filters observed (left panel), and based on the number of filters with 5 σ detections (right panel). For both panels, the blue filled histograms show the galaxies with zphot within zspec ± 2 × 0.02 (1 + zspec ), while the red outlined histogram is for galaxies outside this range. Additionally, we use this probability to classify the acceptable range for photometric redshifts. If a galaxy’s photometric redshift is within ±2 × 0.02 (1 + zspec ), we consider it to be correct; otherwise, the galaxy is removed from our sample (see Figure 3.2). We use this same criteria for selecting cluster members for our final sample. With our correct and incorrect sets defined, we now plot in Figure 3.2 the total number of filters used to observe each galaxy (left panel), and the number of filters with 5 σ detections for each galaxy (right panel). Galaxies considered to be genuine members are plotted as the blue filled histograms, while the remainder are plotted with the red outlined histograms. While we find a fairly even distribution for the galaxies with incorrect photometric 44 3.2. LOW-REDSHIFT CLASH CLUSTERS redshift, more importantly, we find that there is a mimimum limit, in both number of filters observed, and in number of 5 σ filters, for the correct galaxies. Since there is a large peak at 7 filters in both panels, we choose this as a minimum cut for number of filters observed. In summary, in addition to the spectroscopic members identified above, we select photometric members that satisfy the following criteria: P (z) > 0.9; zphot that is within zcluster ± 0.04 (1 + zcluster ); and ≥ 7 filters comprising its HST observations. We subject our membership criteria on 76546 CLASH objects that meet the stellarity≤ 0.05 cut, and in Figure 3.3, we plot the distribution of photometric and spectroscopic redshifts for the 2005 total CLASH members. The 121 zspec members are plotted as the red filled histograms, while the 1884 zphot members are represented by the blue open histograms. Each cluster’s name and redshift are listed, and the latter is shown by the downward arrow. We must combine the 2005 cluster members with the 29876 and 42483 objects for which we could calculate SFRs and stellar masses, respectively. While we determine cluster membership based on spectroscopic and photometric redshifts, we independently calculated SFRs and stellar masses based on one restframe UV and two restframe optical bands, respectively. As we must combine our set of cluster members with our independently calculated sets of SFRs and stellar masses, which we were unable to do for all objects, we caution that our final sample will contain fewer than the 2005 cluster members we find here. 45 3.2. LOW-REDSHIFT CLASH CLUSTERS Figure 3.3: Distribution of spectroscopic (red histogram) and photometric (blue open histogram) CLASH members. In each panel, we list the shortened cluster name, spectroscopic redshift (in parentheses), the number of photometric members, and spectroscopic members, if any. Arrows show the cluster redshift. 46 3.2. LOW-REDSHIFT CLASH CLUSTERS 3.2.5 Galaxy Morphology For consistency with our high-redshift cluster sample, we choose to visually classify the morphology of CLASH galaxies. As there are more than 90000 objects in the entire CLASH catalog, we first reduce this number to a manageable size. We match our list of cluster members (2005 galaxies) to our list of stellar mass (42483 objects) and SFR calculations (29876 objects). This results in an overlap of 1061 CLASH cluster member galaxies with good stellar mass and SFR estimates. Following the method we described in §2.3.5, we simultaneously visually inspect two optical and one NIR HST images, choosing one each of F606W/F625W, F77W/F814W, and F125W/F140W/F160W. We classify galaxies that have smooth elliptical/S0 shapes at ETGs (735/1061 = 69.3%). Galaxies that show clear spiral structure, or irregular or distrubed morphologies are collectively classified as LTGs (244/1061 = 23.0%). Galaxies for which we cannot definitively determine morphology are marked as not classified (82/1061 = 7.7%). We do not include unclassifiable galaxies in the forthcoming analysis, unless we specify that we are selecting galaxies of all types. 47 Chapter 4 Field Galaxy Data and Sample Selection We use the publicly available1 UltraVISTA survey (McCracken et al. 2012; Muzzin et al. 2013) as the basis of our field comparison sample. UltraVISTA is a deep Ks selected survey covering 1.62 deg2 of the COSMOS field (Scoville et al. 2007), and has photometry in 30 bands, used to calculate photometric redshifts and infer stellar masses, and also includes Spitzer photometry in the 24 µm band of MIPS, and all four bands of IRAC (Muzzin et al. 2013). We select galaxies brighter than the UltraVISTA calculated 90% completeness limit of Ks,tot = 23.4 AB. The catalog has also been pruned of stellar objects, contamination from bright stars, and contamination from nearby saturated objects. Our final UltraVISTA field sample contains 8015 galaxies over 0.15 < z < 1.5, of which 6194 are at z < 1. 1 http://www.strw.leidenuniv.nl/galaxyevolution/ULTRAVISTA/Ultravista/Data_ Products_Download.html 48 4.1. REDSHIFTS 4.1 Redshifts Photometric redshifts in UltraVISTA were derived by Muzzin et al. (2013) using EAZY (Brammer et al. 2008), a template fitting software package. A small subset of our final sample (N < 1000) has spectroscopic redshifts from the zCOSMOS surveys (Lilly et al. 2007, 2009). 4.2 Star Formation Rates The UltraVISTA dataset provides rest-frame UV luminosities. We use Equation 3.4 to convert these luminosities into SFRs. Following the process in §2.2.3, for every UltraVISTA galaxy which has a 24 µm MIPS flux, we use Chary & Elbaz (2001) templates to infer total IR luminosities, then convert these to SFRs using Equation 3.1. As noted in §2.3.4, the 24 µm band of MIPS has a large point spread function (∼600 , Rieke et al. 2004), making it difficult to deconvolve IR flux from multiple objects in close proximity. While the effects of this blending have been significantly reduced in the available photometry (Muzzin et al. 2013), to be consistent with our ISCS sample construction, we choose to study isolated galaxies, calculating SFRs only for objects in the UltraVISTA photometric catalog that have no neighbors within 600 . Since AGNs can contribute to the MIR flux of a galaxy (as noted in §2.3.2), we use the IRAC color selection from Stern et al. (2005) to remove from our sample objects likely harboring an AGN. While AGNs may only account for . 5% of all galaxies (see §2.3.2), we conservatively remove objects that do not have measured fluxes in all four IRAC channels (i.e. objects for which we cannot use the Stern et al. (2005) wedge). 49 4.3. STELLAR MASSES 4.3 Stellar Masses Muzzin et al. (2013) estimated stellar masses by using FAST (Kriek et al. 2009) to fit galaxy SEDs to template SEDs. The templates were generated with Bruzual & Charlot (2003) models, using a Chabrier (2003) IMF, and assuming an exponentially declining SFH. Muzzin et al. (2013) calculated an evolving 95% mass completeness limit, which we show in the upper panel of Figure 4.2 with the stepped black line. We cut from our sample any UltraVISTA galaxies that fall below this level. While stellar masses for both CLASH and UltraVISTA are derived using a Chabrier (2003) IMF and Bruzual & Charlot (2003) models, the estimation methods—CMLRs for CLASH galaxies, and SED fitting for UltraVISTA galaxies—differ. We now aim to test the consistency of using stellar masses derived with these two different methods by using the CMLR in Equation 3.2 to calculate stellar masses for our UltraVISTA sample. Following the procedure in §3.2.2, we first select restframe g- and i-band proxies, using the best available UltraVISTA broadband optical and NIR filters. We then calculate, using Equation 3.2, stellar masses for the 8436 isolated UltraVISTA galaxies with no AGN across 0.15 < z < 1.5. In the upper panel of Figure 4.1, we plot the stellar masses derived by both the Muzzin et al. (2013) SED fitting (x-axis) and by using the CMLR from Zibetti et al. (2009, y-axis). To test how well the stellar masses derived by these two methods match, we fit the data using a linear least squares approach, plotting the fit (1 σ uncertainty) with the dark green line (light green shaded region). For comparison, we show a one-to-one relation by the black dashed line. Within the uncertainty, stellar masses derived by these two methods are consistent with being the same. In 50 4.3. STELLAR MASSES Figure 4.1: Upper panel: Stellar masses derived using Equation 3.2 versus stellar masses derived by SED fitting from Muzzin et al. (2013), shown by the red points. The dark green line and light green shaded region show a linear least squares fit to the data, and the 1 σ uncertainty of the fit, respectively. The dashed black line shows a one-to-one relation. Lower panel: Ratio of SED-derived stellar masses to CMLR-derived stellar masses as a function of SED-derived stellar mass. the lower panel, we plot the ratio of SED-derived to CMLR-derived stellar masses as a function of SED-derived stellar masses. We find that 90.0% of the stellar masses 51 4.4. GALAXY MORPHOLOGY calculated with these two methods are within a factor of 2.3 of each other. With this good agreement, we choose to use the existing SED-derived stellar masses from Muzzin et al. (2013). We have verified that none of our qualitative results from Chapter 6 depend on this choice. 4.4 Galaxy Morphology We morphologically classify all our cluster galaxies using visual methods, and while it is ideal to have a consistent classification method for all three samples in this work, as we mention above, the UltraVISTA field sample contains ∼8000 galaxies, so performing visual classification on this set would be incredibly time intensive. We estimate that it takes approximately an hour to classify 50 low-redshift galaxies, or 20 high-redshift galaxies, so for the ∼6200 z < 1 and ∼1800 z > 1 UltraVISTA galaxies, visual classifications would take on the order of 200 hours. Instead, as we noted in §2.3.6, we use the updated, higher-redshift version of the morphological catalog of Cassata et al. (2007) to separate ETGs and LTGs. Cassata et al. (2007) use a non-parametric automatic technique to separate galaxies into early types, described as being elliptical or S0, and late types, which includes all spirals, irregulars, and merging galaxies. Their method is an extension of Cassata et al. (2005), which used concentration, C, asymmetry, A, and clumpiness, S (Conselice 2003; Abraham et al. 2003; Lotz et al. 2004). Cassata et al. (2007) includes Gini, a concentration parameter, which measures how fairly the light is distributed amongst a galaxy’s pixels in the image, and M20 , a concentration parameter that measures the moment of the brightest 20% of the galaxy flux. Cassata et al. (2005) found, using the C-A-S parameter space, a locus for typical ETGs. With the two new parameters, this locus has been updated, 52 4.5. STELLAR MASSES OF FINAL GALAXY SAMPLES and trained against a control sample of 211 galaxies. To test how well the Cassata et al. (2007) method matches with our visual classifications, we randomly select 300 UltraVISTA galaxies, 100 each in redshift slices of 0.15 < z < 0.5, 0.5 < z < 1, and 1 < z < 1.5. With no knowledge of the automated technique’s classifications, we visually classify all 300 using the same method as with our low- and high-redshift cluster galaxies. We find that our overall agreement of 86.3% (259/300) is quite good. The 41 galaxies where the two methods do not agree include 16 galaxies that we deem visually unclassifiable (16/300 = 5.3%), which is similar to the unclassifiable fraction of our CLASH galaxies. When considering redshift, we find that we agree with the Cassata et al. (2007) classifications 84%, 94%, and 81% of the time with increasing redshift. With no discernable trend with redshift, we conclude that our visual classifications are robust against morphological k-correction. 4.5 Stellar Masses of Final Galaxy Samples Now that we have calculated stellar masses for all three of our galaxy samples, we plot, in the upper panel of Figure 4.2, the stellar mass versus redshift for our cluster and field samples. Cluster ETGs (LTGs) are represented by the red (dark blue) points, with the two samples separated by the vertical dot-dashed line at z = 1. Field ETGs and LTGs are shown with the pink and light blue points, respectively. The blue dashed line is the 80% mass completeness limit for the ISCS galaxies, while the black stepped line is the evolving UltraVISTA 95% mass completeness limit, binned in intervals of ∆z = 0.1. In the lower panel, we plot the median stellar mass as a function of redshift for 53 4.5. STELLAR MASSES OF FINAL GALAXY SAMPLES Figure 4.2: Upper panel: Stellar mass versus redshift for CLASH and ISCS galaxies, separated by the vertical dot-dashed line at z = 1, and UltraVISTA field galaxies. Cluster ETGs are plotted as red points, while LTGs are shown with the dark blue points. Field ETGs (LTGs) are represented by pink (light blue) points. The UltraVISTA evolving 95% mass completeness limit is shown, binned in intervals of ∆z = 0.1, by the stepped horizontal line. The 80% mass completeness limit for ISCS is shown by the dashed blue line. Lower panel: Median stellar mass versus redshift. CLASH (ISCS) ETGs and LTGs are represented by the filled (open) red circles and blue squares, respectively, while galaxies of all morphologies are plotted with filled (open) green triangles. UltraVISTA field galaxies are shown by the filled grey points, with ETGs (LTGs) as circles (squares), and galaxies of all morphologies as triangles. all CLASH galaxies, and ISCS and UltraVISTA galaxies above their respective mass completeness cuts shown in the upper panel. CLASH ETGs and LTGs are plotted with the filled red circles (blue squares), while galaxise of all type are shown with 54 4.5. STELLAR MASSES OF FINAL GALAXY SAMPLES the filled green triangles. ISCS galaxies are plotted with open symbols of the same color and shape. Field ETGs (LTGs) are shown by the grey circles (squares), and galaxies of all types are represented by the grey triangles. All median mass errors in this plot are calculated as the quadrature sum of bootstrap resampling and simple Poisson uncertainty. 55 Chapter 5 Infrared-Ultraviolet Star Formation Rate Comparison The available SFRs for our high-redshift cluster sample are derived using 24 µm Spitzer detections. With only UV-derived SFRs for our CLASH cluster sample, in this section, we aim to derive a correction factor that will allow us to directly compare the star-formation activity in both our high- and low-redshift galaxy samples. As UltraVISTA galaxies have both UV- and 24 µm-derived SFRs, and span the entire redshift range of our cluster samples, they provides an excellent test bed to compare the two measurements. 5.1 Motivation Specific comparisons of the IR and UV SFRs in the literature are rather scant. In Figure 5.1, we compare the IR-derived SFR versus UV-derived SFRs based on two 56 5.1. MOTIVATION low-redshift (z . 0.1) studies. The green circles represent 30 galaxies from RosaGonzález et al. (2002), and the blue circles represent 49 galaxies from Iglesias-Páramo et al. (2004), while the red stars show 82 z < 0.1 UltraVISTA galaxies. These latter galaxies are not included in 8015 sample galaxies listed in §4.2 above, as we do no include them in any further analysis beyond this section. −1 Rosa-González et al. (2002) provide UV fluxes in units of erg s−1 cm−2 Å , and IR fluxes in erg s−1 cm−2 Hz−1 . Given these units, we calculate SFRs for the RosaGonzález et al. (2002) sample, taking the redshift1 and flux for each galaxy in their Table 1, and converting the fluxes into luminosities, using the relation L = 4π [D (z)]2 fλ × Cλ (5.1) where D (z) is the luminosity distance, fλ is the flux, and Cλ is a constant which depends on whether we are calculating IR or UV luminosities. In the UV, we have Cλ = 1875 Å (5.2) as it is the midpoint of our UV window. In the IR, we have CIR = 1.7 × c 60 µm (5.3) where c is the speed of light, and the scaling factor of 1.7 is to convert L60µm into total IR luminosity (Chapman et al. 2000). 1 We use the NASA/IPAC Extragalactic Database (NED) to retrieve redshifts. NED is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. 57 5.1. MOTIVATION Iglesias-Páramo et al. (2004) provide their UV and IR observations in AB magnitudes, so we use Equation 3.3 to turn them into luminosities, converting from solar units to erg s−1 . They also provide galaxy distances in physical units, which we use instead of D (z) in Equation 3.3. Finally, we convert the IR luminosities into SFRs using Equation 3.1, and the UV luminosities into SFRs using Equation 3.4. Figure 5.1: IR-derived SFR versus UV-derived SFR for z < 0.1 galaxies from UltraVISTA (red stars), Rosa-González et al. (2002, green circles), and Iglesias-Páramo et al. (2004, light blue circles). In Figure 5.1, we plot log (SF RIR /M yr−1 ) versus log (SF RUV /M yr−1 ) for our z < 0.1 UltraVISTA galaxies (red stars), and the galaxies from Rosa-González et al. (2002, green circles) and Iglesias-Páramo et al. (2004, light blue circles). For each of 58 5.1. MOTIVATION the three galaxy samples, we fit the data, in log space, with a simple linear relation log SF RIR /M yr−1 = a × log SF RUV /M yr−1 + b ± σIR (5.4) where a and b are the slope and y-intercept of the line, respectively, and σIR is the 1 σ uncertainty in log (SF RIR /M yr−1 ). We plot the three fits with the same color as their respective points. We refrain from plotting the uncertainty ranges of our three samples for clarity. We combine the galaxies of all three sets, fitting them with Equation 5.4, which we plot as the solid black line, with the shaded grey region as the 1 σ uncertainty. We find that while there is substantial overlap in the data points for the three samples, the fits have a wide range in slope and y-intercept. We list their coefficients and 1 σ uncertainties in Table 5.1. Table 5.1: Results of log (SF RIR /M yr−1 ) = a × log (SF RUV /M yr−1 ) + b ± σIR linear least squares fit to z < 0.1 galaxy sample Sample Ngal UltraVISTA z < 0.1 82 Rosa-González et al. (2002) 30 Iglesias-Páramo et al. (2004) 49 All 161 a b σ 1.29 0.30 0.50 1.02 1.01 0.71 0.74 0.44 0.59 1.00 0.41 0.70 With the large sample-to-sample scatter, it is likely unwise to choose only one fit in this redshift slice to use as a SF RUV → SF RIR correction. Instead, given that the fit to all 161 galaxies encompasses each individual fit, it seems apparent that choosing as large a sample as possible is the most beneficial. We use this as motivation for the remainder of this chapter. 59 5.2. EMPIRICAL FITS 5.2 Empirical Fits We first fit Equation 5.4 for all 0.15 < z < 1 UltraVISTA galaxies, resulting in a slope and y-intercept of 0.66 and 0.79, respectively, with σIR = 0.47. While the y-intercept is with the range of the first three samples (Table 5.1), the slope is lower than all three, and also significantly lower than the fit to z < 0.1 UltraVISTA galaxies. We take this as suggestive that there is a potential redshift dependence between UV and IR SFRs. Figure 5.2: IR-derived SFRs versus UV-derived SFRs for 0.15 < z < 1 COSMOS/UltraVISTA field galaxies. Points are color coded based on the redshift bin in which they lie (see legend). To determine whether this is the case, we show in Figure 5.2 log (SF RIR /M yr−1 ) versus log (SF RUV /M yr−1 ) for all 0.15 < z < 1 UltraVISTA galaxies, separating them into the redshift bins shown in the bottom right corner. We notice immediately 60 5.2. EMPIRICAL FITS that as redshift increases, the mimimum values of both quantities increase. Figure 5.3: IR-derived versus UV-derived SFRs for UltraVISTA galaxies, split into six non-overlapping redshift bins. For each redshift slice, we fit Equation 5.4 to the data. In each panel, we show the fit and 1 σ uncertainty with a black line and grey shaded region, respectively. 61 5.2. EMPIRICAL FITS Given this result, in Figure 5.3, we show log (SF RIR /M yr−1 ) as a function of log (SF RUV /M yr−1 ) for each redshift slice individually, with the same color scheme as in Figure 5.2. We fit Equation 5.4 for each redshift slice, and plot this as the solid black line in each panel, with the grey shaded region showing the 1 σ uncertainty. In each panel, we list the redshift slice, and the slope, y-intercept, and uncertainty of the fit. While there is potentially a downward trend in a with increasing redshift, we see a steadily increasing b from z = 0.15 → 1. We consider these coefficients to be our correction factors for SF RUV → SF RIR . Rewriting Equation 5.4 yields an alternative form, in linear space SF RIR /M yr−1 = 10b±σIR SF RUV /M yr−1 62 a (5.5) Chapter 6 Analysis 6.1 Projected Radius Given HST ’s small footprint, we only have coverage of the inner ∼650 (∼800) kpc of our CLASH (ISCS) clusters, so our analysis is largely limited to the cores of these clusters. While we can only probe out to rproj ' 650 kpc in our CLASH clusters, we choose to include ISCS galaxies at 650 < rproj < 800 kpc, due to the small ISCS cluster sample. We verify that our qualitative results do not change by including these galaxies. 6.2 Galaxy Morphology Versus Projected Radius While we must select isolated galaxies when measuring IR SFRs (see §2.3.4 and §4.2), we are under no such constraint when determining morphology through visual classification. Thus, despite not including some cluster galaxies when investigating star formation activity, we choose to include all ISCS cluster galaxies, regardless of isolation, when exploring morphology. This increases our ISCS sample size from 104 63 6.2. GALAXY MORPHOLOGY VERSUS PROJECTED RADIUS to 191 galaxies, which helps to reduce the random error inherent in a small sample size. Figure 6.1: Fraction of galaxies classified as early-type versus cluster-centric radius for CLASH (red filled circles) and ISCS (red open circles). The vertical error bars show the binomial error in the fraction, while the horizontal error bars show the sizes of the rproj annuli. The fractions (binomial confidence) for 0.15 < z < 1 and 1 < z < 1.5 UltraVISTA field galaxies are shown by the red horizontal lines (pink shaded regions). In Figure 6.1 we plot the fraction of galaxies classified as early-type as a function of projected cluster radius for CLASH and ISCS galaxies, as red filled circles and red open circles, respectively. The vertical error bars show the binomial error in the fractions, and we note that for some of the CLASH points these errors are small enough to be covered by the point itself. We radially bin our ISCS galaxies into two non-overlapping annuli, with the bin 64 6.3. STAR FORMATION RATE VERSUS PROJECTED RADIUS size shown by the horizontal error bars. Our choice for this binning will be explained in §6.4. We use four non-overlapping bins for our CLASH galaxies, with the inner three bins all being 125 kpc in size. The outermost CLASH bin extends from 375 to 650. We plot the early-type fractions for low- (0.15 < z < 1) and high-redshift (1 < z < 1.5) UltraVISTA field galaxies as the red horizontal lines, with the pink shaded regions showing the binomial errors in the fractions. We find that both the ETG fractions of both CLASH and ISCS clusters decrease with increasing projected radius, dropping by 37% and 29%, respectively, over the range probed. At all radii probed, CLASH clusters consistently have higher ETG fractions than our high-redshift cluster sample, with fractions a factor of ∼1.4 to 1.8 times higher. In the outermost bin of both the CLASH and ISCS clusters, there is a ∼30–45 percentage point difference between the ETG fraction in clusters and in the field, over the same redshift ranges. These large differences are further evidence that we are looking mainly at the cores of these clusters, as we noted in §6.1. Indeed, virial radii have been measured for 19 of the 25 CLASH clusters (Merten et al. 2014); with an average virial radius of 1.2 Mpc, we have typical coverage of only the inner 50%. For ISCS clusters, which have virial radii on the order of 1 Mpc (see reference in §2.4.3), our sampling covers the inner 80%. 6.3 Star Formation Rate Versus Projected Radius As is well known, star formation activity in clusters tends to increase with projected cluster radius. In Figure 6.2 we plot log(SFR/M yr−1 ) versus projected cluster radius for CLASH cluster members. We show the ETGs (LTGs) in the upper (lower) panel. 65 6.3. STAR FORMATION RATE VERSUS PROJECTED RADIUS Figure 6.2: Star formation rate versus radius for CLASH ETGs (upper panel; red points) and CLASH LTGs (lower panel; blue points). The solid black lines show linear least squares fits to the data in each panel. The pink (light blue) shaded regions show the 1 σ uncertainty in the ETG (LTG) fit. To determine if there exists a relation between star formation activity and projected radius, we first perform a Spearman’s rank test on CLASH cluster members, between log (SFR/M yr−1 ) and projected radius, rproj . We find correlation coefficients of rs = 0.19 ± 0.02 and rs = 0.33 ± 0.04, respectively, for ETGs and LTGs, both at the 100% confidence level. Thus only a mild correlation between star formation activity 66 6.4. MEAN STAR FORMATION RATE VERSUS PROJECTED RADIUS and rproj is found. We quantify this correlation through a linear least squares fit to log SFR/M yr−1 = a × rproj + b (6.1) for each morphological subset, where a and b are the slope and y-intercept, respectively. In each panel, the fit is represented by the solid black line, while the shaded pink (light blue) regions show the 1 σ uncertainty in the ETG (LTG) fit. We find that the slope of Equation 6.1 is approximately the same (m ' 0.001) for both ETGs and LTGs. While these results may suggest a radial dependence in star formation activity, the uncertainties are too large to draw any certain conclusions, in agreement with the low correlation coefficients found above. 6.4 Mean Star Formation Rate Versus Projected Radius The large uncertainties that we found when fitting individual SFRs may be reduced by binning our data, as we did in §2.4. In Figure 6.3, we show mean SFR as a function of cluster-centric radius for our low- and high-redshift cluster samples, using filled and open symbols, respectively, for each subset, with the colors and styles shown in the figure legend. Due to the relatively large scatter in ISCS SFRs, we separate these galaxies into two radial annuli, such that each bin has approximately equal signal-tonoise, with the bins shown by the horizontal error bars on the ISCS LTG points. The vertical error bars show the error estimated by 5000 iterations of bootstrap resampling the galaxies in each bin, added in quadrature with a simple Poisson error. With the larger sample size of CLASH galaxies, we are not as constrained when determining the size of our bins. However, in order to both compare with the ISCS clusters, and have the ability to detect a potential radial trend in the data, we choose our inner 67 6.4. MEAN STAR FORMATION RATE VERSUS PROJECTED RADIUS three bins to each be 125 kpc in size, while the outer bin extends from 375 to 650 kpc. We plot the mean SFR of our comparison low- and high-redshift field samples to the right of the vertical dashed line. We estimate errors for this sample in the same manner as with our cluster galaxies. Figure 6.3: Mean SFR versus projected cluster radius for CLASH (filled symbols) and ISCS (open symbols) galaxies, left of the vertical dashed line. The mean SFRs for 0.15 < z < 1 (filled symbols) and 1 < z < 1.5 (open symbols) field galaxies are shown to the right of the vertical dashed line. The horizontal error bars on the ISCS and CLASH LTG points show the size of the radial bins for the high- and lowredshift cluster samples, respectively. The errors in this plot are the quadrature sum of bootstrap resampling and simple Poisson errors. We find that the mean SFR of CLASH galaxies is relatively flat across 0 < rproj < 375 kpc, but we observe a noticeable increase in the outermost bin. Given the errors, we cannot determine if this rise is statistically significant for ETGs, but for LTGs, the rise is at least 0.7 M yr−1 between the two outer bins, a 23% increase. When 68 6.4. MEAN STAR FORMATION RATE VERSUS PROJECTED RADIUS considering all morphologies, the rise is similar, however with an increase of 15%, it is noticeably tempered by the large number of ETGs (59%) that make up the 375 < rproj < 650 kpc annulus. A suprising result—given the large differences between mean SFR for ETGs and LTGs in ISCS clusters—is that in the inner three annuli, mean SFRs for CLASH ETGs and LTGs are indistinguishable within the errors, and it is not until the outermost bin that they become statistically distinct. We are combining galaxies over a very large swath of cosmic time (∼6 Gyr), so as a simple test, we perform our analysis in Figure 6.3, cutting our sample at z = 0.5. However, aside from overall changes in mean SFR with time—as we will discuss in §6.7—we observe roughly the same radial behaviour both above and below this redshift. We surmise that instead of our averaging over 0.15 < z < 1 causing variations in ETG and LTG mean SFRs, we are likely observing the quenching of LTGs as they travel through the cluster, particularly through the cores. This idea is reinforced by Koopmann & Kenney (2004), who found that spiral galaxies in the Virgo cluster have truncated H i disks, relative to spirals in the field, and suggest that interactions with the cluster medium are likely responsible for removing the outer gas from these cluster spirals. With observations of the cluster outskirts, we would be able to more accurately constrain the relative mean SFRs, and determine whether, and at what radial extent, they increase to field levels. However, with the data available to us, we find that at all projected radii probed, CLASH galaxies, regardless of morphology, have mean SFRs lower than that of the field. Even with the increase in mean SFR at rproj > 375 kpc, cluster ETGs have mean SFRs a factor of at least 1.2 times lower than field ETGs. Cluster LTGs have mean SFRs a factor of at least 1.9 times lower than field LTGs. 69 6.5. SPECIFIC STAR FORMATION RATE VERSUS PROJECTED RADIUS We will postpone the main discussion on the evolution of cluster star formation activity until §6.7 and §6.8, but we do note here that similar to CLASH galaxies, ISCS ETGs are quenched relative to the field at all cluster radii, with mean SFRs a factor of 2.0 to 3.0 times lower than that of the 1 < z < 1.5 UltraVISTA ETGs. This result is similar to that found in §2.4.2, where we compared ISCS galaxies to the ETG subset of UltraVISTA field galaxies at 1 < z < 1.5. Unlike high-redshift ETGs, and all low-redshift cluster galaxies, ISCS LTGs show star formation activity that is either at the level of the field, or higher. With our inclusion of disturbed/irregular galaxies in the LTG morphological type, these enhanced SFRs could be due to either very recent merger activity, or even ongoing mergers that are sufficiently progressed to appear as a single object. 6.5 Specific Star Formation Rate Versus Projected Radius Specific star formation rate is the SFR per unit stellar mass, which we calculate by dividing the sum of the SFRs by the sum of the stellar masses. We now turn to the sSFR of our field and cluster samples, as it allows us to examine how efficiently these galaxies are converting their cold gas into new stars. In Figure 6.4, we plot the sSFR for the same samples, using the same radial binning, symbols, and error calculation, as in Figure 6.3. While the mean SFRs of CLASH ETGs and LTGs were nearly indistinguishable at all projected radii, we find that the mass normalized star formation activity of these two subsets are quite different, at all but the most central annulus. Here, the error of the LTG subset is large enough that the two morphological types are formally consistent with having the same sSFR. These errors also preclude us from making any conclusions on any 70 6.5. SPECIFIC STAR FORMATION RATE VERSUS PROJECTED RADIUS Figure 6.4: Specific SFR versus projected cluster radius for CLASH (filled symbols) and ISCS (open symbols) galaxies, left of the vertical dashed line. We use the same radial binning as in Figure 6.3. The sSFRs for 0.15 < z < 1 (filled symbols) and 1 < z < 1.5 (open symbols) field galaxies are shown to the right of the vertical dashed line. sort of radial sSFR trend for LTGs. Within the errors, we cannot determine any form of trend in for CLASH ETGs either, at least in the inner three annuli. The 375 < rproj < 650 kpc bin, however, shows a clear increase above the middle two bins. As we found with mean SFR, the sSFRs of CLASH galaxies are quenched relative to the field at all projected radii. CLASH LTGs have sSFRs a factor of at least 9.4 lower that the sSFR of field LTGs, while for CLASH ETGs, that factor is at least 4.3, relative to field ETGs. 71 6.6. GALAXY MORPHOLOGY VERSUS REDSHIFT 6.6 Galaxy Morphology Versus Redshift In Figure 6.5 we show the fraction of galaxies classified as early-type as a function of redshift for CLASH (red filled circles), ISCS (red open circles), and UltraVISTA (pink filled circles) samples. The vertical error bars show the binomial error in the fractions. For galaxies below (above) z = 1, we use the same redshift bins for the CLASH (ISCS) and UltraVISTA samples. For all samples, the bins are shown by the horizontal error bars on the UltraVISTA points. Figure 6.5: Fraction of galaxies classified as early-type versus redshift for CLASH (red filled circles) and ISCS (open red circles) clusters, and the UltraVISTA field (filled pink circles). Comparison ETG fractions from the literature, spanning 0.33 < z < 1.46, are plotted with the symbols shown in the legend. We include comparison ETG fractions for galaxy clusters from the literature, spanning the vast majority (0.33 < z < 1.46) of the redshift range covered by our 72 6.6. GALAXY MORPHOLOGY VERSUS REDSHIFT Table 6.1: Comparison Cluster ETG Fractions From the Literature Cluster Name Cluster Redshift fETG Ngal Reference Cl 1358+62 0.33 0.71+0.04 −0.04 138 Fabricant et al. (2000) A370 2 0.37 0.48+0.07 −0.07 71 Dressler et al. (1997) Cl 1446+26 0.37 0.57+0.05 −0.05 107 Dressler et al. (1997) Cl 0024+16 0.39 0.61+0.04 −0.04 170 Dressler et al. (1997) Cl 0939+47 0.41 0.55+0.05 −0.05 124 Dressler et al. (1997) Cl 0939+47 2 0.41 0.42+0.07 −0.06 72 Dressler et al. (1997) 0.42 0.58+0.06 −0.06 93 Dressler et al. (1997) 3C 295 0.46 0.72+0.05 −0.06 87 Dressler et al. (1997) Cl 0412-65 0.51 0.54+0.06 −0.06 91 Dressler et al. (1997) Cl 1601+42 0.54 0.45+0.06 −0.05 100 Dressler et al. (1997) Cl 0016+16 0.55 0.73+0.03 −0.04 193 Dressler et al. (1997) Cl 0054-27 0.56 0.52+0.05 −0.05 119 Dressler et al. (1997) CLJ1324+3011 0.76 0.55+0.17 −0.14 22 Lubin et al. (2002) MS1054-0321 0.83 0.78 ± 0.11 130 Postman et al. (2005) RXJ0152.7-1357 0.84 0.72 ± 0.11 125 Postman et al. (2005) Cl 1604+4304 0.90 0.57 ± 0.11 124 Postman et al. (2005) Cl 1604+4321 0.92 0.39 ± 0.10 150 Postman et al. (2005) J1229+0151 0.98 0.80+0.06 −0.08 46 Cerulo et al. (2014) RDCS J0910+5422 1.10 0.38 ± 0.10 146 Postman et al. (2005) RDCS J1252-2927 1.24 0.68 ± 0.14 67 Postman et al. (2005) RX J0849+4452 1.27 0.30 ± 0.09 214 Postman et al. (2005) XMMXCS J2215.9−1738 1.46 0.62 ± 0.17 39 Hilton et al. (2009) Cl 0303+17 CLASH and ISCS clusters. In Table 6.1 we list the additional clusters used in Figure 6.5, their redshifts, ETG fractions (fETG ), number of galaxies in the sample (Ngal ), and the reference for the morphological data. For all comparison samples, we use 73 6.6. GALAXY MORPHOLOGY VERSUS REDSHIFT classifications that match our own as closely as possible. For MS 1054-0321, RX J0152-1357, CL 1604+4304, CL 1604+4321, RDCS J0910+5422, RDCS J1252-2927, and RX J0849+4452, we use the Postman et al. (2005) fractions of galaxies classified as either elliptical or S0, and their errors, which are the quadrature sum of counting statistics and classification uncertainty. For XMMXCS J2215.9−1738 we use the Hilton et al. (2009) fraction of galaxies classified as either elliptical or lenticular (S0), and their error, which is the quadrature sum of Poisson, classification, and cluster membership uncertainties. For the remainder of the classifications, we determine the fraction of ETGs by adding the number of galaxies classified as elliptical with those classified as S0/bulge-dominated, then dividing this sum by the total number of galaxies in the sample. For these fractions, the errors listed in Table 6.1 are our calculated binomial errors. Despite the large cluster-to-cluster scatter, we find good agreement between our morphological fractions and those from the literature. We note that the majority of the 0.37 < z < 0.56 ETG fractions from Dressler et al. (1997) fall below our points. We surmise that this may be a selection effect, as the CLASH clusters are quite morphologically evolved, most having been selected to be massive (Postman et al. 2012). To see if it is the case that there are significant differences in total mass for these two samples, we compare the CLASH and Dressler et al. (1997) clusters for which we could find total mass estimates. 19 of the 25 CLASH clusters have total mass estimates (Merten et al. 2014), with a median of 6.0 × 1013 M . A literature search reveals total mass estimates for five of the Dressler et al. (1997) clusters. Four cluster have total mass estimates from Comerford et al. (2006): A370 2 (1.1 × 1014 M ), Cl 0939+4713 (2.7 × 1013 M ), Cl 0016+1609 (3.3 × 1013 M ), and Cl 0054-27 (2.6 × 1013 M ). Comerford et al. (2006) listed two 74 6.7. MEAN STAR FORMATION RATE VERSUS REDSHIFT or three mass estimates for each cluster, so we take the median value in each case. The remaining mass estimate is for Cl 00214+16 (1.1×1014 M ; Broadhurst et al. 2000). The median total mass of these five clusters is 3.3 × 1013 M , almost a factor of two smaller than the median total mass of CLASH clusters. As others have found (see references in Table 6.1), the vast majority of low- to moderate-redshift cluster galaxies are morphologically early-type. In our clusters, we find that from z = 1.5 → 0.15, the ETG fraction rises from 0.40±0.05 to 0.80 ± 0.03, a 100% increase. In the field, the fraction increases from 0.05±0.01 to 0.31+0.02 −0.01 . With 6 and 12% increases at z = 1.25 and z = 0.36, respectively, we find that the bulk of the morphological evolution in the field occurs over z = 1 → 0.57. Despite this, at all redshifts we study, field ETG fractions are 3 to 10 times lower than those of cluster ETGs. While field galaxies apparently undergo the bulk of their morphological evolution at moderate redshift (0.6 . z . 1), we find that there are no increases larger than 15% between bins for z < 1 clusters. This more gentle increase is in agreement with the concept that cluster populations evolved at earlier cosmic times. Additionally, the 64% increase over z = 1.5 → 1 further suggests that this is an epoch of enchanced galaxy formation and evolution, as suggested in Chapter 2. 6.7 Mean Star Formation Rate Versus Redshift Figure 6.6 shows the mean SFR for our low- and high-redshift cluster samples, with the filled (open) red circles, blue squares, and green triangles representing the CLASH (ISCS) ETGs, LTGs, and galaxies of all morphologies, respectively. We show our field sample, over 0.15 < z < 1.5, as the filled grey circles. We calculate errors as in Figure 75 6.7. MEAN STAR FORMATION RATE VERSUS REDSHIFT 6.3, and use the same redshift binning as in Figure 6.5, except for 1 < z < 1.5, which we combine into one bin. Figure 6.6: Mean SFR versus redshift for ETGs, LTGs, and galaxies of all morphologies in CLASH and ISCS clusters, and the UltraVISTA field. We find that all cluster galaxies are quenched relative to the field at z < 1, and even at 1 < z < 1.5, cluster ETGs have mean SFRs lower than that of field ETGs. Cluster LTGs, however, show a significant increase in star formation activity relative to the field in this high-redshift range. By combining both z > 1 bins, and averaging across all high-redshift galaxies—thus driving down the random error—we find the mean SFR of cluster LTGs is 51.2 ± 10.6 M yr−1 , a factor of 1.8 higher than field galaxies of the same morphology. Even if we cut ISCS LTGs with rproj > 500 kpc—to match the ∼50% virial radius coverage of CLASH clusters—we find that these ‘core’ LTGs have a mean SFR (44.8 ± 11.7 M yr−1 ) only 12.5% lower than that of the 76 6.7. MEAN STAR FORMATION RATE VERSUS REDSHIFT full high-redshift LTG sample, and still a factor of 1.6 higher than the 1 < z < 1.5 field LTG mean SFR. This relative SFR enchancement brings up the mean SFRs of all cluster galaxies to field levels over this redshift range. This relatively high mean SFR, which we previously showed in Figure 2.7, is made even more drastic when compared with mean cluster SFRs at z < 1, further providing evidence for the epoch of enhanced star formation activity at z ∼ 1.4, as proposed by B13. In Figure 6.7, we now plot the mean SFR as a function of redshift for galaxies of all morphology for CLASH (filled green triangles) and ISCS (open green triangles) galaxies. As mentioned in §2.5.2, A14 studied 274 clusters over 0.3 < z < 1.5, measuring IR-derived SFRs through 250 µm stacking. In so doing, they fit the evolution of the mean SFR of both cluster members and field galaxies, using a function of the form y = βeαt (6.2) where t is cosmic time in Gyr, and y = hSF Ri. A14 fit to both the cluster ‘core’ (rproj < 500 kpc), and the ‘core+outskirts’ (rproj < 1000 kpc). Because we are limited to the inner 650 (800) kpc in our CLASH (ISCS) clusters, we choose to use the A14 ‘core’ fit, and limit ourselves to galaxies in the inner 0.5 Mpc of our clusters. With this cut, our low- and high-redshift cluster samples have 1032 and 80 galaxies, respectively. Despite this, by combining LTGs and ETGs, we are still able to finely bin our cluster data, with 6 (4) evenly-sized redshift slices for our z < 1 (z > 1) cluster samples. We use the same redshift binning for our field galaxies, plotting their mean SFRs as the grey triangles. 77 6.7. MEAN STAR FORMATION RATE VERSUS REDSHIFT Figure 6.7: Mean SFR vs redshift for cluster (green triangles) and field (grey triangles) of all types. The solid (dashed) black curve is the mean SFR vs. redshift fit (1 σ uncertainty) for core (rproj < 0.5 Mpc) cluster galaxies over 0.3 < z < 1.5 from A14. The solid grey curve and light grey shaded region is the fit and 1 σ uncertainty, respectively, for field galaxies over the same redshift range from A14. We show the cluster fit and its 1 σ uncertainty, hSF Ri = (810 ± 400) e(−0.66±0.08)t , (6.3) as the solid and dashed curves, respectively. The dark grey curve shows the fit to the A14 field sample, with its 1 σ uncertainty plotted as the light grey shaded band. With our higher-resolution bins, we find a relatively steady increase in mean SFR with redshift for both cluster and field galaxies over 0.15 < z < 1.5. At all redshifts, we see excellent agreement between the A14 fit of stacked Herschel mean SFRs and 78 6.8. SPECIFIC STAR FORMATION RATE VERSUS REDSHIFT both our low-redshift UV→IR-corrected mean SFRs, and our high-redshift 24 µmderived mean SFRs. The mean SFRs of the UltraVISTA field galaxies show moderate agreement to the A14 field fit, with only slight disagreement in four redshift bins. A14, however, fit the field sample with two versions of Equation 6.2, allowing for a break at z = 0.8. The lower mean SFRs between 0.8 . z . 1.1 may indeed be a similar such break, only with a larger magnitude in the UltraVISTA sample. The UltraVISTA galaxies also appear to have a similar field-cluster crossover at z ∼ 1.25. 6.8 Specific Star Formation Rate Versus Redshift As in §6.1, we now turn to specific SFRs, in this case to investigate the cosmic evolution of the star formation efficiency of our cluster galaxies. In Figure 6.8 we plot the mass normalized SFR versus redshift for our cluster and field samples, with the same symbol types, and binning, as in Figure 6.6. In fact, the large uncertainties in sSFR, specifically for cluster LTGs, drove our decision to use only four z < 1 bins. As with their mean SFRs, CLASH cluster galaxies have quenched sSFRs relative to the field—of the respective morphological type—across 0.15 < z < 1. We similarly see a large enhancement in the star formation efficiency of LTGs at z > 1. Despite having a mean SFR a factor of ∼2 greater than that of the field, high-redshift LTGs have median stellar masses 2.5 times higher than z > 1 field galaxies (see Figure 4.2), which accounts for the sSFR that reaches, but does not exceed, the level of the field. Across 0.15 < z < 1 CLASH LTGs have median stellar masses 4 to 10 times lower than CLASH ETGs (see Figure 4.2). So despite seeing little-to-no separation 79 6.8. SPECIFIC STAR FORMATION RATE VERSUS REDSHIFT Figure 6.8: Specific SFR versus redshift for ETGs, LTGs, and galaxies of all morphologies in CLASH and ISCS clusters, and the UltraVISTA field. Redshift bins, and symbols are the same as in Figure 6.7. amongst the mean SFRs of early- and late-types, we find LTG sSFRs in the lower three redshift bins that are 3 to 5 times higher than that of ETGs. Over this same period of z ∼ 0.75 → 0.25, CLASH galaxies show little-to-no evolution regardless of morphology. Within the errors, we cannot determine whether there is a radial trend for LTGs, and ETGs only drop by a factor of 1.6. However, from z ∼ 1.25 to ∼ 0.75, both morphological types experience significant decreases in their sSFR, with ETGs dropping nearly an order of magnitude (9.4), and LTGs falling by a factor of 15.6. Figure 6.9 shows sSFR versus redshift for galaxies of all morphologies in the cluster (green triangles) and field (grey triangles), using the same redshift binning as in Figure 6.7. A14 used Equation 6.2, letting y = sSF R, to fit the evolution of their measured 80 6.8. SPECIFIC STAR FORMATION RATE VERSUS REDSHIFT Figure 6.9: Specific SFR versus redshift redshift for CLASH and ISCS cluster galaxies of all morphologies (filled and open green triangles, respectively), and UltraVISTA field galaxies (grey triangles). The solid (dashed) black curve is the sSFR vs. redshift fit (1 σ uncertainty) for core (rproj < 0.5 Mpc) cluster galaxies over 0.3 < z < 1.5 from A14. The solid grey curve and light grey shaded region is the fit and 1 σ uncertainty, respectively, for field galaxies over the same redshift range from A14. The gold shaded region shows the main sequence fit to the sSFR redshift evolution from Elbaz et al. (2011). cluster (rproj < 0.5 Mpc) and field sSFRs. We show these fits using the same style as we did in Figure 6.7. Elbaz et al. (2011) measured the sSFR redshift evolution of galaxies observed in the northern and southern fields of the Great Observatories Origins Deep Survey. Their best fit sSFR evolution is described by h i sSF R Gyr−1 = 26 × t−2.2 81 (6.4) 6.8. SPECIFIC STAR FORMATION RATE VERSUS REDSHIFT where t is cosmic time. They classify galaxies with sSFR greater than twice Equation 6.4 to be starbursts, and galaxies below half Equation 6.4 to be galaxies with ‘significantly lower’ star formation. This gives a range of h i 13 × t−2.2 ≤ sSF R Gyr−1 ≤ 52 × t−2.2 (6.5) which defines, according to Elbaz et al. (2011), the ‘main sequence’ of galaxies. We plot the main sequence of Equation 6.5 as the gold shaded region in Figure 6.9. We find that our UltraVISTA sSFRs lie in excellent agreement with the Elbaz et al. (2011) main sequence, while they are almost uniformly higher than the field sSFR fit from A14. A14 imposed a mass cut of M? = 1.3 × 1010 M across their entire field sample, while we adopt an evolving mass cut that ranges from M? = 1.0 × 1010 M at z = 1.5 down to M? = 2.3 × 108 M at z = 0.15, as shown in the top panel of Figure 4.2. Our inclusion of lower mass galaxies mostly accounts for the somewhat enhanced sSFR values of our field sample relative to A14’s field fit. By imposing stellar mass cut of M? = 1.3×1010 M , we find that the sSFR of our field sample agrees within the errors with the A14 field sSFR fit in all but three redshift bins, a moderate improvement over the results with the UltraVISTA evolving stellar mass cut. We find that from z = 1.5 to z ∼ 0.35 cluster galaxies exhibit a steady quenching, with good agreement to the A14 cluster core sSFR fit down to z ∼ 0.9. In the lowestredshift bin, we see a slight enhancement in the sSFR of cluster galaxies relative to the sSFR at z ∼ 0.35. However, we suggest that the lack of completeness in high-mass CLASH galaxies in this redshift bin (see Figure 4.2) is the cause of this apparent rise in sSFR. 82 Chapter 7 Summary and Conclusions We have used a sample of 11 high-redshift (1 < z < 1.5) ISCS clusters, 25 low-redshift (0.15 < z < 1.0) CLASH clusters, and 8015 low- and high-redshift UltraVISTA field galaxies to explore the evolution of cluster star formation activity over 0.15 < z < 1.5. We selected CLASH members by either spectroscopic redshifts, if available, or with photometric redshifts. We similarly preferentially used spectroscopic redshifts to select ISCS members; red-sequence membership was used for galaxies with no spectroscopic redshift. We separated our cluster galaxies into late- and early-type morphologies by visually inspecting them in optical and NIR HST images. We used the updated CAS/Gini/M20 classifications from Cassata et al. (2007) to determine field galaxy morphologies, using the same two coarse morphological bins. We visually inspected a random subset of 300 UltraVISTA galaxies, and found good agreement (86%) between the two methods. We used 24 µm imaging to measure the dust-obscured SFRs for our ISCS and 83 UltraVISTA samples, excluding galaxies that had nearby neighbors, or likely harbored an AGN. We also used rest-frame UV photometry to calculate UV SFRs for UltraVISTA and CLASH galaxies. We used the two sets of SFRs from UltraVISTA and performed a linear least squares fit to the IR vs UV SFRs, binned in redshift. We used the fit as an empirical calibration to correct the CLASH UV SFRs, which allowed them to be directly compared to the ISCS and UltraVISTA IR-derived SFRs. We used SED template fitting to estimate stellar masses for our ISCS sample. The same methodology was used to calculate stellar masses in the UltraVISTA catalog. We derived stellar masses for our CLASH sample by using a Zibetti et al. (2009) CMLR, taking advantage of the 16 available HST filters to measure rest-frame (g − i) colors, and calculate i-band luminosities, across the entire CLASH redshift range. We began our analysis in Chapter 2 by focusing on the star formation activity of 1 < z < 1.5 cluster and field galaxies. By comparing the mean SFR of ISCS ETGs and LTGs, and UltraVISTA ETGs, we found that high-redshift cluster ETGs are significantly quenched, relative to both field early types, and cluster late types. Despite their relatively quenched SFRs, ETGs still account for 12.7% of the total SFR observed in ISCS clusters. We found that the fraction of ISCS ETGs that are star forming (SF R > 26 M yr−1 ) decreases from 28.6% at 1.35 < z < 1.5 to 10.5% by 1.16 < z < 1.35, as more of the early-type cluster population becomes quenched. Over the same period, however, their mean and specific SFRs remain relatively unchanged, which we suggested implies that some mechanism must be acting to increase the SFRs of the remaining star-forming ETGs. Not only are ISCS ETGs experiencing the same overall star formation activity during this epoch, but also the fraction of cluster galaxies that are morphologically early type increases from 34.1% to 55.9%. 84 We concluded that these results were consistent with a scenario where new ETGs are being created through major merging, and that the mergers must be fairly recent and gas-rich to account for the enhanced star formation activity of the star-forming subset. Because of the small footprint of HST, we were only able to observe the inner ∼ 650 kpc of CLASH clusters. Based on virial radius estimates by Merten et al. (2014), which give an average virial radius of 1.2 Mpc for the 19 CLASH clusters they studied, we are only considering the inner 50% of low-redshift clusters. At all radii that we can probe, we find that all z < 1 cluster galaxies have mean SFRs that are quenched relative to the field. Furthermore, at cluster-centric radii ≤ 375 kpc, the mean SFR of LTGs is indistinguishable, within the errors, from that of ETGs. We suggest that the LTGs interior to ∼ 400 kpc are likely being quenched due to environmental processes. In the outermost radial bin, the mean LTG SFR rises above that of CLASH ETGs, and also closer to the field value. However, the mean SFR of the CLASH LTGs in this range is still a factor of 1.9 lower than that of LTGs in the field. This further emphasizes that we are observing well into the cluster cores. We investigated the morphology evolution of cluster galaxies, and found steadily increasing cluster ETG fractions with time, rising from 40% at 1.25 < z < 1.5 to 80% at 0.15 < z < 0.36, with the majority of that increase coming over z = 1.5 → 1, a period of evidently major growth in the ISCS early-type population. We also compared our cluster ETG fractions against 22 clusters drawn from the literature, and found generally good agreement, albeit with a fair amount of scatter in the individual cluster fractions. As our ISCS and CLASH clusters were building up their ETG populations, their 85 7.1. WHAT’S NEXT? star formation activity was strongly declining. From z ∼ 1.5 → 0.15, the mean SFR of cluster galaxies of all morphologies drops from 63.3±7.5 M yr−1 to 0.5±0.1 M yr−1 . We found that this result matched quite well with the A14 fit in Equation 6.3. We previously showed in §2.5.2, for ISCS galaxies, that the 24 µm-derived SFRs agreed quite well with the 250 µm-derived SFRs from A14. We have now additionally found that rest-frame UV-derived SFRs, corrected by an empirical SF RIR versus SF RUV relation also show excellent agreement to the A14 fit. 7.1 What’s Next? • To augment our analysis of high-redshift ISCS galaxies, we would like to attempt to deconvolve 24 µm SFRs for non-isolated galaxies. We propose using PyGFit (Mancone et al. 2013), a program designed to extract SEDs from crowded images by first generating models from higher-resolution images (e.g. ACS), then fitting them to the lower resolution images (e.g. MIPS). • We would like to find archival MIPS imaging, for as many of the CLASH clusters as possible, in order to calculate true 24 µm SFRs. This would allow us to be more confident in our low-redshift SFR measurements. We would also be able to test how accurate our empirical SF RUV → SF RIR relations (§5.1) are when correcting the UV SFRs of our CLASH galaxies. Specifically, given the vastly different morphological makeup of the field, we could determine if these relations, calculated using field galaxies, are truly applicable to cluster galaxies. • We would like to take advantage of the 16 bands of HST observations of CLASH galaxies to perform full SED fits to determine stellar masses. 86 7.1. 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