STAR FORMATION AND AGN ACTIVITY IN GALAXY CLUSTERS FROM z = 1–2: A MULTI-WAVELENGTH ANALYSIS FEATURING HERSCHEL/PACS

The ISCS consists of over 300 galaxy cluster candidates over the redshift range 0.1 < z < 2. The cluster candidates are identified based on infrared-selected galaxy catalogs as three-dimensional (3D) overdensities in (R.A., decl., photometric redshift) space using a wavelet detection algorithm and photometric redshifts (Brodwin et al. 2006, 2013; Elston et al. 2006; Eisenhardt et al. 2008) derived from deep optical B_wRI imaging from the NOAO Deep Wide-field Survey (NDWFS; Jannuzi & Dey 1999) and Spitzer/IRAC imaging from the IRAC Shallow Survey (ISS; Eisenhardt et al. 2004). The ISCS spans 8.5 square degrees in the Boötes field and includes >100 cluster candidates at z > 1, over 20 of which have been spectroscopically confirmed (Stanford et al. 2005; Brodwin et al. 2006, 2011, 2013; Elston et al. 2006; Eisenhardt et al. 2008; Zeimann et al. 2013). A follow up survey, the IDCS, was conducted using deeper IRAC data from the Spitzer Deep Wide-field Survey (SDWFS; Ashby et al. 2009). Two z ∼ 1.8 IDCS clusters have been spectroscopically confirmed to date (Stanford et al. 2012; Zeimann et al. 2012).

Targeted follow up of high-significance, spectroscopically confirmed ISCS clusters at z > 1 have found halo masses in the range M₂₀₀ = (1–5) × 10¹⁴ ${M}_{\odot }$ using X-ray observations (Brodwin et al. 2011, 2016) and weak lensing (Jee et al. 2011). These direct measurements are consistent with statistical analyses of the full ISCS sample, which find mean halo masses of M₂₀₀ ∼ (5–8) × 10¹³ ${M}_{\odot }$ using clustering (Brodwin et al. 2007) and halo mass ranking simulations (Lin et al. 2013) with no significant redshift evolution in the median halo mass (Alberts et al. 2014). Given these typical halo masses, the ISCS clusters have a characteristic virial radius of ∼1 Mpc at z > 0.5, which we will adopt for r₂₀₀ throughout this study. Though this work will primarily focus on ISCS clusters, we additionally include in our study one cluster at z = 1.75 from the IDCS, which has a halo mass of M₂₀₀ ≈ 4 × 10¹⁴ ${M}_{\odot }$ from X-ray and Sunyaev–Zel'dovich effect measurements (Brodwin et al. 2012, 2016; Stanford et al. 2012).

In this work, we concentrate our analysis on 11 spectroscopically confirmed clusters from the ISCS/IDCS that we observed with Herschel/PACS. These clusters, which span the redshift range 1 < z < 1.75, are listed in Table 1, which includes available halo mass measurements and additional references. In Section 3.3, we utilize ∼250 ISCS clusters at z > 0.5 plus 3 IDCS clusters at z ∼ 1.8 for an analysis of the AGN fraction in clusters.

Table 1.  Cluster Sample with Deep Herschel/PACS Imaging

Cluster ID	Short ID	R.A.	Decl.	Spectroscopic	Nspec	M200	Additional
		(J2000)	(J2000)	Redshift		(1014 M⊙)	References
ISCS J1432.4+3332a	ID1	14:32:29.18	33:32:36.0	1.113	26	${4.9}_{-1.2}^{+1.6}$ b	(1), (2), (3), (4)
ISCS J1434.5+3427a	ID2	14:34:30.44	34:27:12.3	1.238	19	${2.5}_{-1.1}^{+2.2}$ b	(1), (3), (4), (5)
ISCS J1429.3+3437a	ID3	14:29:18.51	34:37:25.8	1.262	18	${5.4}_{-1.6}^{+2.4}$ b	(2), (3), (4)
ISCS J1432.6+3436a	ID4	14:32:38.38	34:36:49.0	1.350	12	${5.3}_{-1.7}^{+2.6}$ b	(2), (3), (4)
ISCS J1434.7+3519a	ID5	14:34:46.33	35:19:33.5	1.374	10	${2.8}_{-1.4}^{+2.9}$ b	(2), (3), (4)
ISCS J1432.3+3253a	ID6	14:32:18.31	32:53:07.8	1.396	10	...	(3), (4)
ISCS J1425.3+3250a	ID7	14:25:18.50	32:50:40.5	1.400	7	...	(3), (4)
ISCS J1438.1+3414a	ID8	14:38:08.71	34:14:19.2	1.413	16	${2.2}_{-0.6}^{+0.7}$ c	(2), (3), (4), (6), (7)
ISCS J1431.1+3459a	ID9	14:31:08.06	34:59:43.3	1.463	6	...	(3), (4)
ISCS J1432.4+3250a	ID10	14:32:24.16	32:50:03.7	1.487	11	${2.5}_{-0.9}^{+1.5}$ c	(3), (4), (7)
IDCS J1426.5+3508	ID11	14:26:32.95	35:08:23.6	1.75	7	4.1 ± 1.1d	(8), (9), (10), (11)

Notes.

^aCluster has Hα measurements from HST grism spectroscopy (Section 2.2) and targeted deep MIPS imaging (Section 2.4.1). ^bWeak lensing mass measurement from Jee et al. (2011). ^cX-ray mass measurement from Brodwin et al. (2011). ^dSZ mass measurement from Brodwin et al. (2012).

References. (1) Elston et al. (2006), (2) Eisenhardt et al. (2008), (3) Brodwin et al. (2013), (4) Zeimann et al. (2013), (5) Brodwin et al. (2006), (6) Stanford et al. (2005), (7) Brodwin et al. (2011), (8) Brodwin et al. (2012), (9) Gonzalez et al. (2012), (10) Stanford et al. (2012), (11) Brodwin et al. (2016).

Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.

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Targeted follow up campaigns by our group have obtained spectroscopic redshifts for galaxies and AGNs in z > 1 clusters using multi-object Keck optical spectroscopy and Wide Field Camera 3 (WFC3) slitless NIR grism spectroscopy from the Hubble Space Telescope (HST; Kimble et al. 2008). The reader is directed to Brodwin et al. (2013), Zeimann et al. (2013), and references therein for a detailed description of the targeted spectroscopy. Some spectroscopic redshifts are additionally provided by the AGN and Galaxy Evolution Survey (AGES; Kochanek et al. 2012), which includes spectroscopic redshifts for galaxies at z ≲ 0.6 and AGNs at z ≲ 3. Spectroscopic confirmation of a cluster is based on the detection of at least five galaxies with spectroscopic redshifts in the range $\pm 2000(1+\langle {z}_{\mathrm{spec}}\rangle )$ km s⁻¹ and within a cluster-centric radius of 2 Mpc. The number of spectroscopic redshifts in the main cluster sample for this work can be seen in Table 1.

We present new targeted imaging from the Herschel Space Observatory (Pilbratt et al. 2010) Photodetector Array Camera and Spectrometer (PACS; Poglitsch et al. 2010) at 100 and 160 μm, obtained during Open Time 2 observing (PID: OT2_apope_3). Eleven clusters were observed from 1 < z < 1.8 with integration times ranging from 270–5040 s over two to four pointings in order to reach the same L_IR limit at all redshifts. Each resulting map is centered on an individual cluster, with the exceptions of ID6 and ID10, which were observed in one map due to their small angular separation (∼4 arcmin). Each map covers a FOV of 7' × 7', a physical size of 2–3 Mpc in radius. The central 5' × 5' of this area is uniform in depth, with a small loss in sensitivity toward the edges of the map (see Appendix A).

Data reduction is performed using Unimap v5.4.0 (Traficante et al. 2011; Piazzo et al. 2012, 2015), a generalized least-squares (GLS; Lupton 1993) mapmaker. The individual astronomical observation requests (AORs) are first processed up to Level 1 in HIPE v10 (Ott 2010) and then converted to a Unimap usable format using UniHIPE. Next, pre-processing, which removes baseline drifts, offsets, jumps, and spikes due to cosmic rays, is applied, followed by the GLS mapmaker. Finally, astrometry is corrected for each map by stacking point sources from a 5σ MIPS 24 μm catalog and removing any offsets in the stack. The final PACS maps are in Jy pixel⁻¹ with 1'' and 2'' pixel sizes at 100 and 160 μm, respectively. The rms sensitivity ranges from ∼0.5–2 mJy in the central 5' × 5' region of each map (see Table 6 in Appendix A).

Given the resolution of PACS (FWHM ∼ 67 at 100 μm and 11'' at 160 μm), we expect the majority of sources and all cluster galaxies in our maps to be point sources. PACS 100 μm flux densities are extracted using point-spread function (PSF) fitting at the known positions of IRAC sources in the SDWFS 5σ 4.5 μm catalog. We note that although it is more common in the literature to use MIPS 24 μm sources as priors for Herschel source extraction (e.g., Magnelli et al. 2013), deep MIPS imaging is not available for ID11. In addition, using IRAC priors allows us to create more complete source catalogs as some PACS sources may not be detected by MIPS. Within the redshift range relevant to this work, these MIPS dropouts will occur preferentially at z ∼ 1.3 due to silicate absorption (e.g., Magdis et al. 2011), making it more challenging to interpret changes in cluster populations around this epoch. Given the depth of the SDWFS catalog, there is typically one IRAC source per PACS 100 μm beam and we use visual inspection to identify the rare cases of blending. Blended sources are rejected from our samples. We note that this excludes close merger systems, which likely contribute to cluster SF activity. The local background is estimated in postage stamps around each IRAC positional prior and flux density uncertainties are measured from the full residual maps. We test the robustness of our catalogs using Monte Carlo simulations and determine that we can measure accurate flux densities using priors down to the 2σ level. We construct a 2σ 100 μm point source catalog and use these catalog positions as priors to extract the flux densities of sources in the PACS 160 μm maps following the same PSF-fitting procedure. Based on simulations, our 100 μm catalog is 70% complete at 5.5–1.3 mJy at z = 1–1.75, which corresponds to ${L}_{{\rm{IR}}}\sim 7\times {10}^{11}\;{L}_{\odot }$ over the redshift range probed. For more details about the observations, source extraction, and completeness simulations, see Appendix A.

The Boötes field contains a wealth of multi-wavelength observations with photometry from the X-ray to the radio. The reader is referred to Chung et al. (2014) for a full description of the UV-to-MIR photometry used to derive the photometric redshifts used in this work (Section 2.5). In the following, we describe the ancillary MIR-FIR photometry, as well as the X-ray observations used.

2.4.1. Spitzer/IRAC, Spitzer/MIPS, and Spitzer/IRS Imaging

2.4.2. Herschel/SPIRE Imaging

2.4.3. Chandra X-Ray Imaging

In this work, we adopt the new photometric redshift catalog of Chung et al. (2014). This catalog incorporates NIR observations from the Infrared Boötes Imaging Survey (IBIS; Gonzalez et al. 2010) with previously available UV-MIR photometry, providing greater accuracy for photometric redshifts at z > 1.5. Using up to 17 photometric bands, the photometric redshifts are calculated through SED fitting, using non-negative linear combinations of empirically derived templates (for complete details, see Assef et al. 2010). An R-band luminosity prior from the Las Campanas Redshift Survey (Lin et al. 1996) was used to avoid unphysical fits. The templates include a characteristic elliptical, spiral, and irregular (starburst), as well as an AGN template, which is introduced with a variable amount of internal reddening. Each source was first fit with only galaxy templates. Then they were fit by galaxy+AGN templates and an F-test was used to see if the addition of an AGN component significantly improved the goodness of fit (see Chung et al. 2014 for a detailed discussion). Stellar and brown dwarf templates were also fit in order to identify Galactic sources.

Aside from the improved photometric redshift accuracy at z > 1.5 arising from the inclusion of the IBIS NIR photometry, the advantage of this catalog over the Brodwin et al. (2006) photometric redshift catalog, which has been used in previous ISCS/IDCS and other Boötes field analyses, is that the SED fitting procedure provides a measure of the AGN content in each galaxy, described in Section 2.5.1. We have found that the Chung et al. (2014) and Brodwin et al. (2006) photometric redshift catalogs are consistent within their stated errors for galaxies out to z = 1.5. For the purposes of this work, we limit our photometric redshift catalog to sources with 4.5 μm fluxes greater than 5.2 μJy (5σ). After removing stars and brown dwarfs, this catalog contains 281,779 sources.

Chung et al. (2014) reports photometric redshift uncertainties of σ/(1 + z) = 0.040 for galaxies and σ/(1 + z) = 0.169 for AGNs, with 5% outlier rejection. As their analysis only includes sources that were unambiguously galaxies or unambiguously AGNs as determined during the SED fitting, we further test the photometric redshift uncertainties in two ways. First, we do a comparison with available spectroscopic redshifts for all galaxies and AGNs, including "composite" objects that have a significant contribution from both. We find σ/(1 + z) = 0.040 for sources dominated by host galaxy emission and σ/(1 + z) = 0.214 for sources dominated by AGN emission when these composite galaxies are included (see Section 2.5.1 and Appendix B). Second, since few spectroscopic redshifts are available for galaxies at z > 1.5, we use pair statistics (Quadri & Williams 2010; Huang et al. 2013) to test the photometric redshift uncertainties at high redshift. Using this technique, we measure σ/(1 + z) = 0.054 for galaxies, with no significant dependence on redshift up to z ∼ 2. For further details on these tests, see Appendix B.

2.5.1. The Contribution from AGN: F_gal

As described in Assef et al. (2010) and Chung et al. (2014), the AGN content of a source can be determined through SED fitting by measuring the contributions of the best-fit galaxy and AGN templates to the UV-MIR SED. Specifically, we quantify the ratio of the UV-MIR luminosity from best-fit galaxy templates to the total UV-MIR luminosity (galaxy+AGN templates): F_gal ≡ L_gal/L_total, with F_gal = 0.5 providing a useful dividing line between sources whose luminosity is primarily from an AGN (F_gal < 0.5) versus those whose luminosity is primarily provided by the (host) galaxy (F_gal > 0.5). This technique takes advantage of a broad wavelength range, providing a more complete selection in terms of AGN type and the balance between AGN and host galaxy emission, than AGN indicators that use only limited wavelength windows or colors (e.g., Hickox et al. 2009; Mendez et al. 2013; Chung et al. 2014). Checking against spectroscopic redshifts, Assef et al. (2010) found that the ability of this SED fitting technique to measure F_gal is relatively independent of the derived photometric redshift; even galaxies with large uncertainties in their photometric redshift have a small uncertainty in F_gal.

A detailed analysis of how this technique compares to other common AGN indicators such as X-ray emission, spectral features, and MIR colors (e.g., Lacy et al. 2004; Stern et al. 2005) was presented in Chung et al. (2014). We briefly summarize their findings here. X-ray-selected AGNs were found to have a large range of F_gal, with a tighter correlation for sources that are optically compact. This is consistent with the large scatter in the MIR colors of X-ray AGNs, which can lie outside of MIR AGN color space, and is likely due to soft X-ray observations being sensitive to lower luminosity AGNs (Cardamone et al. 2008; Gorjian et al. 2008; Eckart et al. 2010; Mendez et al. 2013). AGNs at z > 1 identified through optical spectral features, on the other hand, show a strong correlation with F_gal, with ∼80% of these AGNs having F_gal < 0.5.

Luminous AGNs will have MIR SEDs that resemble a power law and thus occupy a particular region of MIR color space (e.g., Lacy et al. 2004; Stern et al. 2005; Donley et al. 2012; Kirkpatrick et al. 2013). Chung et al. (2014) looked at unambiguous AGNs, as identified through SED fitting, in MIR color space, finding that 57% and 75% were recovered by the Lacy et al. (2004) and Stern et al. (2005) AGN selections, respectively, and 32% by the more conservative Donley et al. (2012) selection. Particularly for deeper MIR observations, it has been shown that a more conservative selection is necessary to remove dusty SFG interlopers from AGN samples selected by IRAC colors (Donley et al. 2012; Kirkpatrick et al. 2013). This is consistent with our expectation that the SED fitting identifies lower luminosity AGNs and composite objects with a significant host contribution in the MIR.

In Figure 1, we show the MIR colors of all galaxies in our photometric redshift catalogs in the relevant redshift range, 1 < z < 1.8. We break the galaxies into four categories: F_gal < 0.3 ("AGN-dominated"), 0.3 < F_gal < 0.5 ("AGN-composite"), 0.5 < F_gal < 0.7 ("host-composite"), and F_gal > 0.7 ("host-dominated"). In general, sources trend from the region of MIR color space traditionally associated with AGNs to that of SFGs as a function of increasing F_gal, with significant scatter and a significant fraction of AGN-dominated and AGN-composite source that would not be found in MIR selections alone. We note that X-ray AGNs are found throughout MIR color space, with the X-ray AGN fraction decreasing with increasing F_gal. The number of AGNs detected in the X-ray is small for our data set as the X-ray imaging available across the Boötes field is very shallow (see Section 2.4.3).

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Stellar mass estimates are available for sources in the SDWFS IRAC catalog (see Brodwin et al. 2013), derived with optical and MIR photometry using iSEDfit (Moustakas et al. 2013), a Bayesian SED fitting code, with Bruzual & Charlot (2003) population synthesis models and a Chabrier (2003) initial mass function (IMF). Though individual mass errors are typically reported by iSEDfit to be <0.2 dex, a mass error of 0.3 dex is adopted in this work for all stellar mass estimates in order to account for systematic uncertainties. At z > 1, this stellar mass catalog is 80% complete above log (${M}_{\star }/{M}_{\odot }$) = 10.1 (see Figure 3 in Brodwin et al. 2013). We note that these stellar mass estimates were derived assuming the photometric redshifts calculated in Brodwin et al. (2006), which introduces an additional uncertainty. This uncertainty, however, is small compared to the overall systematic uncertainties.

Sources with spectroscopic redshifts are considered cluster members if they meet the criteria set in Eisenhardt et al. (2008): a spectroscopic redshift within 2000 km s⁻¹ of the systemic cluster velocity and within 2 Mpc of the cluster center. We only consider robust spectroscopic redshifts when considering membership. For sources with photometric redshifts, cluster membership is determined based on constraining the integral of the normalized probability distribution function (PDF). For sources with F_gal > 0.5, we integrate over the photometric redshift uncertainties calculated using pair statistics, σ = $0.054(1+{z}_{{\rm{cl}}})$ (see Section 2.5 and Appendix B). Photometric redshift cluster members are thus identified as sources within 2 Mpc of the cluster center which satisfy the following criterion:

$\begin{eqnarray}&&{\int }_{{z}_{\mathrm{cl}}-0.054(1+{z}_{\mathrm{cl}})}^{{z}_{\mathrm{cl}}+0.054(1+{z}_{\mathrm{cl}})}P(z){dz}\geqslant 0.3,\end{eqnarray} \tag{ 1 }$

where P(z) is the PDF of the photometric redshift.

Previous studies have found that the AGN fraction in galaxy clusters increases by two orders of magnitude from z = 0 to z = 1 (Galametz et al. 2009; Martini et al. 2013). In order to account for the contribution to cluster SF from the AGN population, we identify AGNs as galaxies with F_gal < 0.5. To determine cluster membership for these sources, we again adopt the photometric redshift uncertainty σ = $0.054(1+{z}_{{\rm{cl}}})$ in Equation (1), rather than σ = $0.214(1+{z}_{{\rm{cl}}})$ as was measured for F_gal < 0.5 sources using pair statistics (see Appendix B), which would produce a sample strongly contaminated by field AGNs. This conservative approach gives us a better census of the total SF and AGN activity of cluster galaxies with minimal contamination; however, we note that our F_gal < 0.5 cluster member sample likely suffers from incompleteness and bias toward composite AGNs, as it is more difficult to measure photometric redshifts for SEDs completely dominated by AGN power-law emission.

Clusters ID6 (z = 1.396) and ID10 (z = 1.487) have an angular separation of only 4 arcmin (∼2 Mpc) between their centers. Given the photometric redshift uncertainties, ∼30% of potential cluster members in the overlapping regions satisfy the cluster membership criteria for both clusters. In order to avoid double counting, we assign galaxies to the cluster for which they have the highest integrated photometric redshift PDF at the redshift of that cluster.

Finally, the spectroscopic and photometric cluster member lists are checked for overlap. Roughly 60% of the spectroscopic redshift cluster members have a match in the photometric redshift catalog and therefore a measurement of F_gal. The remaining 40% are largely faint galaxies observed with the HST/WFC3 grism, which obtained a depth ∼10 times fainter than the SDWFS IRAC catalog at 4.5 μm. The total number of cluster members identified is 569, with 142 spectroscopic redshift members and 328 (99) photometric redshift members with F_gal > 0.5 (F_gal < 0.5). Roughly 10% of spectroscopic redshift members do not have a stellar mass estimate and cannot be included in analyses that require a stellar mass or where a stellar mass cut is applied.

Spectroscopic redshift cluster members are matched to the SDWFS IRAC catalog (search radius ${r}_{s}=1^{\prime\prime}$) to determine stellar masses, IRAC, and PACS counterparts. The spectroscopic cluster members are also checked for a counterpart in the photometric redshift catalog; if found, then UV-MIR is available through matched photometry catalogs (see Chung et al. 2014 for more details). IRS 16 μm and MIPS 24 μm counterparts are searched for in the IRS and deep MIPS catalogs, using ${r}_{s}=1^{\prime\prime}$ as the source extraction is based on IRAC priors (Brodwin et al. 2013). If a MIPS detection is not available from the deep imaging because of incompleteness or being outside the field of view of the deep MIPS images, then the MAGES catalog is searched for a >3σ detection within 3'' of the IRAC position.

Photometric redshift cluster members automatically have matches to the full UV-MIR matched photometric catalogs used by Chung et al. (2014) and to the stellar mass catalog. MIPS counterparts are determined as described above and PACS counterparts come directly from the IRAC priors. X-ray detections were identified using a variable search radius to account for the off-axis PSF degradation. All cluster members are visually inspected for blending with nearby bright sources in the PACS 100 μm maps and removed from the cluster catalogs if blended. Table 2 contains statistics for cluster members with PACS 100 μm detections and Table 3 contains the photometry for these members.

Table 2.  Characteristics of PACS 100 μm Detected Cluster Members

	Number	LIR Range (Median)	SFR Range (Median)
	Detected at 100 μma	(1011 ${L}_{\odot }$)	(${M}_{\odot }$ yr−1)
Spectroscopic Redshift Membersb	27	4–25 (7)	50–360 (100)
Photometric Redshift Members (Fgal > 0.5)	68	4–40 (8)	60–590 (120)
Photometric Redshift Members (Fgal < 0.5)	34	4–30 (10)	54–460 (170)

Notes.

^aWithin 2 Mpc in projected radius. ^bApproximately ∼40% of spectroscopic redshift members do not have a match in the photometric redshift catalog and therefore do not have a measured F_gal.

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Table 3.  Photometry of PACS 100 μm Detected Cluster Members

Cluster	Name	R.A.	Decl.	Redshift	Redshift	Cluster-centric	F4.5 μm	${F}_{16\mu {\rm{m}}}$	${F}_{24\mu {\rm{m}}}$	${F}_{100\mu {\rm{m}}}$	${F}_{160\mu {\rm{m}}}$
ID		(J2000)	(J2000)		Type	Radius (Mpc)	(mJy)	(mJy)	(mJy)	(mJy)	(mJy)
ISCS J1432.4+3332	J143230.1+332927	14:32:30.15	33:29:27.7	1.07	Photoz	1.6	0.032 ± 0.001	...	0.21 ± 0.02	4.9 ± 2.2	3 ± 6
ISCS J1432.4+3332	J143217.2+332959	14:32:17.17	33:29:59.6	1.13	Photoz	1.8	0.030 ± 0.001	...	...	7.8 ± 2.1	15 ± 5
ISCS J1432.4+3332	J143228.9+333040	14:32:28.91	33:30:40.3	1.111	Specz	1.0	0.030 ± 0.001	...	0.20 ± 0.02	3.8 ± 1.8	7 ± 4
ISCS J1432.4+3332	J143235.5+333054	14:32:35.47	33:30:54.6	1.02	Photoz	1.1	0.045 ± 0.001	0.29 ± 0.01	0.36 ± 0.02	7.8 ± 2.0	5 ± 4
ISCS J1432.4+3332	J143228.4+333152	14:32:28.42	33:31:52.3	1.10	Photoz	0.4	0.030 ± 0.001	0.09 ± 0.01	0.06 ± 0.01	4.8 ± 2.1	5 ± 5
ISCS J1432.4+3332	J143234.2+333239	14:32:34.25	33:32:39.9	1.098	Specz	0.5	0.030 ± 0.001	0.02 ± 0.01	...	3.5 ± 1.5	...
ISCS J1432.4+3332	J143227.4+333254	14:32:27.40	33:32:54.0	1.03	Photoz	0.2	0.069 ± 0.001	0.32 ± 0.01	0.57 ± 0.03	12 ± 2.0	8 ± 4
ISCS J1432.4+3332	J143246.0+333258	14:32:46.04	33:32:58.1	1.03	Photoz	1.8	0.021 ± 0.001	...	0.12 ± 0.02	4.3 ± 2.1	2 ± 5
ISCS J1432.4+3332	J143242.4+333339	14:32:42.41	33:33:39.1	1.19	Photoz	1.5	0.038 ± 0.001	0.47 ± 0.03	0.42 ± 0.02	9.7 ± 1.9	23 ± 8
ISCS J1432.4+3332	J143231.5+333344	14:32:31.53	33:33:44.2	1.18	Photoz	0.6	0.042 ± 0.001	0.34 ± 0.01	0.38 ± 0.02	8.4 ± 2.5	3 ± 4
...
IDCS J1426.5+3508	J142649.1+350948	14:26:49.09	35:09:48.2	1.84	Photoz	1.9	0.014 ± 0.001	...	0.14 ± 0.04	1.8 ± 0.8	5 ± 2
IDCS J1426.5+3508	J142620.2+351059	14:26:20.17	35:10:59.5	1.68	Photoz	1.9	0.026 ± 0.001	...	0.49 ± 0.05	7.6 ± 0.7	8 ± 3
IDCS J1426.5+3508	J142630.3+351103	14:26:30.26	35:11:03.5	1.62	Photoz	1.4	0.021 ± 0.001	...	0.18 ± 0.05	2.6 ± 0.6	7 ± 2

Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.

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In order to examine the impact of environment on the shape of the SED of star-forming galaxies, we use our Herschel-selected sample to compare the average SED of cluster galaxies to empirical SED templates developed using field galaxies. For this analysis, we select cluster galaxies within the virial radius (r < 1 Mpc) to minimize contamination from field galaxies, requiring a detection in at least the 4.5 μm and 100 μm bands, and a stellar mass of log (M_⋆/M_⊙) ≥ 10.1. We further break this sample into three subsamples by membership (spectroscopic or photometric redshift) and AGN contribution.

We start with our most robust cluster sample: spectroscopic redshift members. Since there are only a few sources in this subsample with evidence for an AGN, we remove sources that have F_gal < 0.5, are X-ray detected, or have an MIR SED dominated by power-law emission as determined by visual inspection, leaving a purely star-forming sample of 15 spectroscopic cluster members that meet the criteria outlined above. The optical to FIR SEDs of these galaxies can be seen in Figure 2, normalized at rest-frame 4.5 μm. Overlaid in large black circles are the average luminosities weighted by the inverse variance in the IRAC 3.6, 4.5, 5.6, and 8.0 μm, IRS 16 μm, MIPS 24 μm, and PACS 100 and 160 μm bands. We additionally display the average luminosity in the SPIRE bands, which is determined through stacking following the procedure outlined in Alberts et al. (2014). Because of the small number of stacked objects, we do not attempt to correct for boosting in the SPIRE bands due to source confusion and clustering (see Viero et al. 2013; Alberts et al. 2014), so these points are formally upper limits even though they are detected in the stack. Cluster members in ID7 are not included in the stack because they are outside the SPIRE FOV.

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We compare our cluster galaxy average photometry to a library of empirically derived SED templates developed for IR-luminous field galaxies at z ∼ 1–2 (Kirkpatrick et al. 2012, 2015). This library is chosen for several reasons: (1) the field galaxies surveyed are IR-selected, with well sampled FIR SEDs from Herschel photometry; (2) they cover similar ranges in redshift (z ∼ 1−2) and expected L_IR as our cluster galaxies; and (3) IRS spectroscopy was used to determine the AGN content of each galaxy through SED decomposition in the MIR (Pope et al. 2008; Kirkpatrick et al. 2012). The library contains templates for a range (0%–100%) of AGN content in the MIR SED, with measured contributions from the AGN to the total L_IR for each template.

We perform the comparison using χ² minimization between the average photometry (with bootstrapped errors) in the IRAC to PACS bands and each template in the Kirkpatrick et al. (2015) library, each of which represents a SFG at high redshift with increasing AGN content. Though we have optical photometry, the templates are not well optimized in the optical and so we do not include these bands in the comparison. The SPIRE stacked photometry is also not included in the fits as we cannot accurately account for bias due to source confusion and clustering; instead, we check that our stacks, which are formally upper limits, are consistent with the best-fit templates. We find that for spectroscopic redshift cluster members the average photometry is well described by a purely star-forming field galaxy template (reduced χ² ∼ 0.2; Figure 2).

We repeat this analysis for photometric redshift cluster members within the virial radius (r ∼ 1 Mpc; Figure 3) splitting the sources into subsamples with F_gal > 0.5 (19) and F_gal < 0.5 (6), where the latter consists of sources with >50% contribution from the AGNs in the optical-MIR SED. We find that the F_gal > 0.5 cluster galaxies are well fit (reduced χ² ∼ 2) by the purely star-forming template, as was found for the spectroscopic redshift sample. The F_gal < 0.5 sample is best fit (reduced χ² ∼ 3) by a template with 50% of its MIR SED coming from an AGN. This is consistent with our AGN selection (based on the optical-MIR SED) and our use of photometric redshifts for cluster membership, which will be biased against the most luminous AGNs (see Section 2.5.1).

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These results demonstrate that the near- to far-infrared SEDs (∼1–200 μm) of cluster galaxies show no significant deviation from the SEDs of field galaxies at similar redshifts, on average. Additional detections on the Rayleigh–Jeans tail of the dust emission are required to directly quantify dust properties of cluster galaxies and probe the full FIR SED; submillimeter observations in ID11 at z = 1.75 cluster will be presented in a future work (S. Alberts et al. 2016, in preparation).

We utilize the best-fit templates to derive total infrared luminosities for each galaxy in each subsample by normalizing by the PACS 100 μm flux and then integrating under the template to measure L_IR ≡ L[8–1000 μm]. Though we do not have individual detections at longer wavelengths to show that the FIR SED is fully consistent, we find that the stacked submillimeter flux densities from SPIRE are consistent with the best-fit templates. We also note that the total L_IR is dominated by the shorter wavelength FIR emission (∼70% of the IR luminosity is accounted for at 8–100 μm) where we have good coverage of the SED. We calculate the SFR following the relation from Murphy et al. (2011b)

$\begin{eqnarray}&&\mathrm{SFR}\;\ [{M}_{\odot }\;{\mathrm{yr}}^{-1}]=1.47\times {10}^{-10}\;{L}_{\mathrm{IR}}^{\mathrm{SF}}\;[{L}_{\odot }],\end{eqnarray} \tag{ 2 }$

where ${L}_{{\rm{IR}}}^{{\rm{SF}}}$ is the contribution to the L_IR coming from SF only. For F_gal > 0.5 sources, ${L}_{{\rm{IR}}}^{{\rm{SF}}}$ is equal to the total L_IR. For F_gal < 0.5 sources, SED decomposition of the best-fit template was used to determine that 21% of the L_IR is due to the AGN and so we correct for this factor as ${L}_{{\rm{IR}}}^{{\rm{SF}}}=0.79\times {L}_{{\rm{IR}}}^{{\rm{tot}}}$ (for more details, see Kirkpatrick et al. 2015). Table 4 gives the stellar mass, F_gal, ${L}_{{\rm{IR}}}^{{\rm{SF}}}$, and SFR for our PACS-detected cluster members. Our L_IR to SFR conversion assumes a Kroupa (2001) IMF, which has a similar normalization as the Chabrier IMF assumed for our stellar mass estimates (see Speagle et al. 2014).

Table 4.  Derived Properties of PACS 100 μm Detected Cluster Members

Cluster	Name	Redshift	log M⋆a	Fgal	LSFIR	SFR
ID			(${M}_{\odot }$)		(1011 ${L}_{\odot }$)	(${M}_{\odot }$ yr−1)
ISCS J1432.4+3332	J143230.1+332927	1.07	10.8	0.95	7 ± 3	100 ± 40
ISCS J1432.4+3332	J143217.2+332959	1.13	10.9	0.59	10 ± 3	150 ± 40
ISCS J1432.4+3332	J143228.9+333040	1.111	10.6	0.87	5 ± 2	80 ± 40
ISCS J1432.4+3332	J143235.5+333054	1.02	10.9	0.78	10 ± 3	150 ± 40
ISCS J1432.4+3332	J143228.4+333152	1.10	10.7	0.78	6 ± 3	90 ± 40
ISCS J1432.4+3332	J143234.2+333239	1.098	10.7	1.00	5 ± 2	70 ± 30
ISCS J1432.4+3332	J143227.4+333254	1.03	11.2	0.47	15 ± 3	220 ± 40
ISCS J1432.4+3332	J143246.0+333258	1.03	10.6	0.74	6 ± 3	80 ± 40
ISCS J1432.4+3332	J143242.4+333339	1.19	10.9	0.45	12 ± 3	180 ± 40
ISCS J1432.4+3332	J143231.5+333344	1.18	11.1	0.52	11 ± 3	160 ± 50
...
IDCS J1426.5+3508	J142649.1+350948	1.84	9.3	0.09	7 ± 3	100 ± 50
IDCS J1426.5+3508	J142620.2+351059	1.68	10.0	0.29	29 ± 3	430 ± 50
IDCS J1426.5+3508	J142630.3+351103	1.62	10.8	0.77	12 ± 3	170 ± 40

Note.

^aAn error of 0.3 dex is adopted for all stellar mass measurements (see Section 2.6).

Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.

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Table 2 summarizes the L_IR and SFR characteristics of our Herschel-selected cluster members. The distribution of SFRs and specific SFRs (SSFR ≡ SFR/M_⋆) as a function of stellar mass and radius can be seen in Figure 4. The dotted line denotes the stellar mass cutoff (log (M_⋆/M_⊙) ≥ 10.1) which is adopted in the following analyses. The dot–dashed line indicates the 50% SFR completeness level for star-forming galaxies (∼80 ${M}_{\odot }$ yr⁻¹), based on the PACS completeness and the SFG template. This SFR completeness limit will be ∼20% lower for F_gal < 0.5 sources, marked by red dots, as their PACS flux (and thus L_IR) includes a contribution from AGN emission. This AGN contribution to the L_IR is removed to determine the SFRs seen in Figure 4, as described in Section 3.1. For reference, the main sequence (MS) of galaxies is shown at z = 1, z = 1.5, and z = 2 (dashed lines), adopted from Elbaz et al. (2011), and corrected to a Kroupa (2001) IMF and the Murphy et al. (2011b) L_IR to SFR conversion:

$\begin{eqnarray}&&{{\rm{SSFR}}}_{\mathrm{MS}}\;[{{\rm{Gyr}}}^{-1}]=36.2\times {t}_{{\rm{cosmic}}}^{-2.2},\end{eqnarray} \tag{ 3 }$

where t_cosmic is the cosmic time since the Big Bang. It should be noted that our sample is SFR-limited and so does not probe the MS for the full range of M_⋆ above our mass cutoff. Assuming a scatter around the MS of a factor of 2 (Elbaz et al. 2011) and our 50% completeness limit, we are unlikely to detect MS galaxies below log (M_⋆/M_⊙) < 10.8 [log (M_⋆/M_⊙) < 10.5] at z = 1 [z = 1.5].

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Figure 4 shows that our spectroscopically confirmed cluster members have a comparable range in SFR, M_⋆, and SSFR to photometric redshift members. Similarly, cluster members with significant AGN content (F_gal < 0.5), denoted by red dots, span a similar region as those without, though the former has a higher median SFR (Table 2). Our full Herschel-selected cluster galaxy sample falls in the general region that is described by the MS in field galaxy studies (e.g., Rodighiero et al. 2010, 2014; Elbaz et al. 2011), indicating that cluster galaxies have similar SFRs and SSFRs as field galaxies at this epoch. However, as galaxies in cluster cores show significant quenching at z ≲ 1 (e.g., Patel et al. 2009; Muzzin et al. 2012; Brodwin et al. 2013), with lower average SFRs relative to the field (Alberts et al. 2014), there are clearly important environmental differences driving the evolution of cluster galaxies. Our results are in good agreement with the analysis of MIPS-selected cluster galaxies drawn from an overlapping cluster sample in Brodwin et al. (2013).

3.2.1. SF as a Function of Cluster-centric Radius

We next look at the fraction of PACS 100 μm-detected cluster members as a function of projected radius (Figure 5, top). We split our cluster sample into two redshift bins: 1 < z < 1.37 and 1.37 < z < 1.75, allowing us to compare to the analysis of MIPS-selected cluster galaxies presented in Brodwin et al. (2013). In order to highlight environmental trends, we make the assumption that our outermost radial bin is a good approximation of the field and normalize by this value. We find that, moving from low to high redshift, the fraction of IR-luminous cluster galaxies flattens into the cluster cores, going from ∼50% of the field value at z < 1.37 to consistent within 1σ with the field value at z > 1.37 in the innermost radial bin. Within the very centers of the clusters (r < 250 kpc), ∼30% of the cluster galaxies are PACS-detected at z > 1.37, versus ∼15% in the lower redshift clusters. Figure 5 (top) demonstrates that (1) IR-luminous cluster galaxies at z ≳ 1.4 are present in the cluster cores in numbers approaching that in the field, and (2) over a relatively short timescale (≲1 Gyr), a significant fraction of these galaxies must be quenched below our detection limit, in excess of the normal evolution of field galaxies along the MS as a function of redshift. We repeat this analysis for cluster members with log(M_⋆/M_⊙) > 10.8 to confirm that these trends are not driven by our sensitivity to the MS as a function of redshift.

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We compare our results in Figure 5 (top) to Figure 6 in Brodwin et al. (2013), which shows a strong transition from z = 1 to z = 1.5 in the fraction of SFGs. This includes a rising SFG fraction in the cluster cores (r < 0.5 Mpc), in excess of the field, at z ≳ 1.4 for MIPS-selected galaxies with ${L}_{{\rm{IR}}}\gtrsim 3\times {10}^{11}\;{L}_{\odot }$. Our findings are in good agreement with these results as a function of redshift, with the Herschel-selected IR-luminous galaxy fraction (including galaxies with AGNs, which were removed from the Brodwin et al. 2013 samples) flattening into the cluster cores at z ≳ 1.4. We do not see a comparable rise in the IR-luminous fraction above the field, however, which implies that environmentally driven processes resulting in the excess primarily boost galaxies up to moderate SFRs at this epoch. Constraints on the IR-luminous fraction in clusters at higher redshift are necessary to disentangle whether such processes cannot produce bright IR galaxies or whether these galaxies simply evolved earlier (i.e., downsizing).

The middle and bottom panels of Figure 5 show the average SFR and SSFR as functions of projected radius and redshift. Errors are determined using bootstrapping and thus represent the spread in the SF properties of the full population. The average SFR is consistent with being flat with projected radius for both redshift bins (Figure 5, middle), with cluster galaxies in the cluster cores having field-like SFRs.

In the bottom panel, we see that $\langle {\rm{SSFR}}\rangle$ follows a different trend with projected radius between the two redshift bins. At lower redshift, we see a decline in the average SSFR by a factor of ∼2 into the cluster cores, indicating that these galaxies are not forming stars at the same rate, for their mass, as their counterparts in the field. At higher redshift, we see no such environment effect in the SSFR, which is flat into the cluster cores, a trend previously observed in Brodwin et al. (2013). This result demonstrates that the IR-luminous cluster galaxies are undergoing the transition first proposed in Brodwin et al. (2013), which roughly marks the epoch in which the quenching of cluster galaxies in the cluster cores becomes effective.

In Figure 6, we break our sample down by stellar mass, in two bins $10.1\lt \mathrm{log}({M}_{\star }/{M}_{\odot }$) < 10.8 and log (M_⋆/M_⊙) > 10.8. We find that lower mass galaxies show a modest increase in their $\langle {\rm{SFR}}\rangle$ and $\langle {\rm{SSFR}}\rangle$ into the cluster cores. Again the errors are determined by bootstrap resampling and encapsulate the spread in the population. This result supports the idea that any boosting of SF by the cluster environment is primarily apparent in less extreme galaxies, in terms of stellar mass and infrared luminosity, as suggested by our comparison with the Brodwin et al. (2013) SFG fraction. The higher mass galaxies [log (M_⋆/M_⊙) > 10.8], on the other hand, show a flat $\langle {\rm{SFR}}\rangle$ and decreasing $\langle {\rm{SSFR}}\rangle$ into the cluster cores. This decrease is driven by our lower redshift clusters, which we verify by placing this high-mass cut on our 1.37 < z < 1.75 bin only, which yields a flat trend for $\langle {\rm{SSFR}}\rangle$ with projected radius. We do not have enough cluster galaxies in the cluster cores to split the lower mass sample by redshift and preserve good statistics.

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Figures 5 and 6 demonstrate that the cluster environment is having multiple effects on the IR-luminous cluster population. The IR-luminous fraction drops with increasing cosmic time over the redshift range probed, indicating that quenching is becoming effective, and occurring on timescales fast enough to remove IR-luminous galaxies from our sample from z ∼ 1.5 to z ∼ 1. For galaxies in the cluster cores that remain above our detection limit in both redshift bins, the average SFR is flat with radius, while the average SSFR declines with radius in our lower redshift bin, indicating that the environment is suppressing SFRs relative to stellar mass in excess of what we expect for the evolution of field galaxies on the MS. This decline is primarily driven by the higher mass galaxies, while the lower mass galaxies show signs of increased SFRs and SSFRs over what we expect in the field, suggestive of the boosting in the SFG fraction seen in Brodwin et al. (2013). These results argue for a complex interplay between environment, SFR, and stellar mass, with a general trend from field-like SF to increasingly effective quenching in the IR-luminous cluster population during this epoch.

3.2.2. Probing Deeper: Stacking on the PACS Maps

In the previous section, we analyzed the IR-luminous cluster population relative to the environment. Now we look at SF for the full stellar-mass-limited cluster population (log (M_⋆/${M}_{\odot })$ ≥ 10.1) using a stacking analysis on the PACS 100 μm maps. As each cluster map has a different depth, stacking is performed on each map separately. We combine cutouts centered on the positions of each cluster member and then extract the stacked flux using the same source extraction outlined in Section 2.4.2. Because we are interested in the properties of the full population, we do not remove detected sources from the stack and perform a median stack, which is robust against a small number of bright sources. The noise within the region we stack is within ∼15% of the central value, making median stacking more robust than noise-weighted stacking for this analysis. In the case where most sources are near the noise limit, which describes our sample, the results of a median stack are representative of the mean of the population (e.g., White et al. 2007). Sources are separated by F_gal during stacking and the final SFRs are obtained through applying the SFG and AGN templates to the appropriate portion of the stacked flux.

The combined stacked SFRs and SSFRs of all clusters in two redshift bins can be seen in Figure 7 as a function of projected radius. In the lower redshift clusters, we see, contrary to the IR-luminous cluster population (Figure 5), a significant decrease in the average SFR in the cluster cores relative to the field. Since we are stacking on stellar-mass-limited galaxy samples, this is expected due to the increase in the fraction of quiescent and/or quenching galaxies in the cluster environment. In the higher redshift clusters, however, the stacked average SFR is flat with cluster-centric radius, indicating that the fraction of quiescent or low SFR galaxies does not yet exceed the field value at this epoch. Similarly, the flat average SSFR indicates that cluster galaxies at z > 1.4 are still forming stars for their stellar mass at rates similar to field galaxies at the same epoch. These trends are fully consistent with the behavior in the PACS-detected cluster galaxy population seen in this work, as well as in Brodwin et al. (2013), and further demonstrates that our cluster sample is undergoing a transition from the epoch of active SF to effective quenching during this era.

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We compare these results to the SF properties measured for stellar-mass-limited galaxy samples through stacking on SPIRE imaging for the full ISCS cluster sample (Alberts et al. 2014). The decrease in the average SFR as a function of redshift in this study (Figure 7) is consistent with the evolution of the average SFR found in that study ($\langle {\rm{SFR}}\rangle \sim {(1+z)}^{5.6}$). However, Alberts et al. (2014) found that the average SFRs of cluster galaxies at $\langle z\rangle =1.2$ are comparable to the stacked $\langle {\rm{SFR}}\rangle$ of field galaxies at the same redshifts ($\langle {\rm{SFR}}\rangle \sim 25\;{M}_{\odot }$ yr⁻¹), while here we find a decrease below the field value at z ≲ 1.4. This apparent discrepancy can be resolved given that the clusters in this work are on the high end (M_halo ≳ 10¹⁴ M_⊙) of the halo mass distribution of the full ISCS sample. In addition, the SPIRE stacking analysis found an enhancement in the $\langle {\rm{SFR}}\rangle$ of (stellar-mass-limited) cluster galaxies over the field at $0.5\lt r/\mathrm{Mpc}\lt 1$ in clusters at $\langle z\rangle =1.4$, driven by lower mass cluster galaxies. In this work, we see increased $\langle {\rm{SFR}}\rangle$ and $\langle {\rm{SSFR}}\rangle$ for the lower mass IR-luminous galaxy population at r ≲ 0.5 Mpc. We suggest that these differences between the cluster subsample in this work and the analysis of the full ISCS sample demonstrate downsizing effects where more massive clusters, preferentially targeted for additional study in this work, quench SF earlier.

3.2.3. Cluster-to-cluster Variations

Given that identifying and obtaining multi-wavelength observations of large samples of clusters at z > 1 is a costly endeavor, multiple studies to date have relied on observations of individual clusters to analyze cluster galaxy populations. Here we look at variations in SF from cluster to cluster in order to quantify the diversity of high-redshift clusters within a uniformly selected sample with similar halo masses.

In the previous section, we looked at the star-forming fraction and average SF properties of IR-luminous and stellar-mass-limited cluster galaxy samples (Figures 5–7). The uncertainties in these quantities were determined through bootstrap resampling and thus encompass the intrinsic scatter due to variation in the cluster galaxy populations. These bootstrapped uncertainties suggest substantial scatter from cluster to cluster. In Figure 8, we look at the total SFR per area for both PACS-detected cluster members and for stellar-mass-limited cluster members, derived by multiplying the stacked $\langle {\rm{SFR}}\rangle$ by the total number of stacked sources, for each cluster. We find that the full range of total SF within the virial radius (≲1 Mpc) spans an order of magnitude (Σ SFR per area ∼10–200 ${M}_{\odot }$ yr⁻¹ arcmin⁻²), with some clusters showing very little excess SF in the central 1 Mpc while others show up to ∼7 times the amount of SFR per area as in the outer 1–2 Mpc. By contrast, we find less variation at larger radii (1 < r/Mpc < 2), with an integrated SFR per area range of ∼20–50 ${M}_{\odot }$ yr⁻¹ arcmin⁻². Looking at the outlier-resistant median and interquartile range values (third quartile minus the first quartile; see Table 5), we can see that the interquartile range of the Σ SFR per area from detections and stacking in the central 1 Mpc is 1.5–3 times larger than in the outer radial bin. To determine if this variation is due to differences in richness from cluster to cluster, we repeat this analysis for the total SFR per galaxy for IR-luminous cluster members within the virial radius and at 1 < r/Mpc < 2. We find that the interquartile range within the virial radius remains ∼1.5 times larger than in the outer radial bin, indicating that we cannot account for the variation in total SF between clusters by richness alone. The scatter in the total SF is likely due to a combination of the stochasticity of processes such as galaxy mergers and differences in the dynamical states of the clusters. In addition, the scatter within the virial radius relative to the outskirts suggests that the environmental impact on these galaxies is not dominated by pre-processing among infalling groups, otherwise we might expect similar variation between the two radial bins. This is consistent with theoretical studies which indicate that pre-processing only becomes important after z ∼ 0.5–1 (McGee et al. 2009).

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Table 5.  Statistics—Total Star Formation in Clusters

	Median	First	Third	Interquartile
		Quartile, Q1	Quartile, Q3	Range (Q3-Q1)
Σ SFR per area (${M}_{\odot }$ yr−1 arcmin−2)
PACS 100 μm Detected Cluster Members (r < 1 Mpc)	49	28	51	23
PACS 100 μm Stacked Cluster Members (r < 1 Mpc)	58	40	78	38
PACS 100 μm Detected Cluster Members (1 < r/Mpc < 2)	24	18	32	14
PACS 100 μm Stacked Cluster Members (1 < r/Mpc < 2)	33	26	39	13
ΣSFR/Mhalo (${M}_{\odot }$ yr−1 per 1014 ${M}_{\odot }$)
PACS 100μm Detected Cluster Members (r < 1 Mpc)	207	123	254	131

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Given the large variation in total SF from cluster to cluster, we verify that no single cluster is driving the average trends seen in the previous section by removing each cluster one at a time from our z > 1.37 bin and recalculating the star-forming fraction, average SFR, and SSFR. We find that the trends in these quantities represent the general cluster sample, though with large variations demonstrated by the bootstrap resampling uncertainties.

When comparing the total SF within the virial radius from PACS detections versus from stacking, we find a median ratio of 0.8, ranging from 0.6 at the first quartile to a maximum of 1. We find no strong dependence on redshift for this ratio. For the majority of our clusters, therefore, the bulk of the SF is occurring in the IR-luminous cluster members with SFR ≳ 100 ${M}_{\odot }$ yr⁻¹, i.e., our PACS-detected, IR-luminous galaxies are the typical star-forming cluster galaxies at this epoch. We discuss the implications of this further in Section 4.

In Figure 9, we examine the halo mass-normalized integrated SFR (ΣSFR/M_halo where M_halo = M₂₀₀) to investigate the relation between SF properties and total mass in clusters. Halo masses for our clusters are listed in Table 1. Though halo mass estimates are sometimes available from multiple techniques, we adopt mass values from X-ray or SZ observations where available and weak-lensing-derived masses otherwise. Three of our clusters have no independent mass measurement and are assigned ${{\rm{M}}}_{{\rm{halo}}}=2.5\times {10}^{14}\;{M}_{\odot }$, the median value of our X-ray and SZ mass estimates. The total SFR is calculated from the sum of the SFRs of the IR-luminous cluster galaxies with log (M_⋆/M_⊙) ≥ 10.1 within the cluster virial radius (r < 1 Mpc). We find that, given a relatively small halo mass range of $\sim (2\mbox{--}5)\times {10}^{14}\;{M}_{\odot }$, the ΣSFR per unit halo mass has a full range of about an order of magnitude, with a median of 207 ${M}_{\odot }$ yr⁻¹ per 10¹⁴ ${M}_{\odot }$ and an interquartile range of 131 ${M}_{\odot }$ yr⁻¹ per 10¹⁴ ${M}_{\odot }$(Table 5).

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For comparison, we show the ΣSFR per unit halo mass derived from IR-luminous galaxies in two massive (${M}_{200}\sim (3\mbox{--}4)\times {10}^{14}\;{M}_{\odot }$) clusters at z ∼ 1.5. XDCP J0044.0-2033 at z = 1.58 (Santos et al. 2011, 2015; Tozzi et al. 2015) was observed with Herschel/PACS to have ΣSFR/M_halo = 954 ${M}_{\odot }$ yr⁻¹ per 10¹⁴ ${M}_{\odot }$ within the virial radius for IR-luminous galaxies with SFR ≳ 165 ${M}_{\odot }$ yr⁻¹. Observations at 450 and 850 μm of XCS J2215.9-1738 at z = 1.46 (Ma et al. 2015) found ΣSFR/M${}_{{\rm{halo}}}={460}_{-150}^{+210}\;{M}_{\odot }$ yr⁻¹ per 10¹⁴ ${M}_{\odot }$ within the virial radius for a SFR limit of ∼100 ${M}_{\odot }$ yr⁻¹. These clusters are comparable to our main sample in terms of redshift, mass, and FIR depth; however, they were discovered via their X-ray emission rather than selected in the IR. We find that they have higher SF for their halo mass than the average of the clusters in this work. Given the variation we find from cluster to cluster within our sample, it is difficult to say whether this difference can be accounted for by different cluster selections. This comparison emphasizes that analyses of individual clusters are challenging to interpret and that clusters viewed in isolation may bias our understanding of cluster evolution. Measurements of the mass-normalized integrated (infrared) SFR are also available in the literature for Cl 0218.3-0510 at z = 1.62 (Pierre et al. 2012; Smail et al. 2014); however, we do not show this cluster as its lower mass makes it less comparable to our main cluster sample.

The evolution of ΣSFR/M_halo with redshift has been quantified using cluster surveys in the literature up to z ∼ 1, with some disagreement as to the extrapolation of the behavior of massive halos at higher redshift (e.g., Geach et al. 2006; Koyama et al. 2011; Popesso et al. 2012, 2015; Webb et al. 2013). We compare here to four relations that demonstrate this disagreement, keeping in mind that we have made no effort to correct for differences in cluster selection, galaxy selection, or depth in L_IR. Geach et al. (2006), examining two clusters at z ∼ 0.5, extrapolated a trend of ΣSFR/M${}_{{\rm{halo}}}\sim {(1+z)}^{7}$, which they note closely follows the evolution of infrared galaxies in the field up to z ∼ 1.5 (see also Cowie et al. 2004). A similar result, ΣSFR/M ${}_{{\rm{halo}}}\sim {(1+z)}^{5.4\pm 1.5}$, derived for IR-luminous cluster galaxies up to z ∼ 1 for $\sim 3\times {10}^{14}\;{M}_{\odot }$ halos was found by Webb et al. (2013). As seen in Figure 9, these trends indicate that SF in massive clusters is at or suppressed below the field at z < 1. However, at higher redshifts, cluster SF may surpass SF in lower mass halos, indicating a true reversal in the SFR-density relation (see also, e.g., Hilton et al. 2010; Tran et al. 2010; Brodwin et al. 2013; Alberts et al. 2014; Santos et al. 2015). By contrast, Popesso et al. (2015), using X-ray-selected clusters up to z ∼ 1 (10^14–15 ${M}_{\odot }$, blue dotted line) and massive groups (10^13–14 ${M}_{\odot }$, green dotted line) up to z ∼ 1.5, predict a flattening in ΣSFR/M_halo, with SF in the most massive halos always suppressed below the global field level. This can be seen in Figure 9, where the field value is represented by the solid line and gray shaded region which show the evolution of the ΣSFR/M_halo of all galaxies up to z ∼ 2, quantified as the observed SFR density presented in Behroozi et al. (2013) divided by the mean comoving matter density of the universe. We note that this global relation is formally a lower limit, as we have made the assumption that all matter is locked in halos that host galaxies. In actuality, the fraction of matter locked in occupied halos depends on the local density field and so our mass-normalized integrated SFR for the field may be underestimated by up to a factor of 2–3 (Faltenbacher et al. 2010). Since this factor is fairly uncertain, we do not correct for it.

In comparison with these relations, cluster and field, we see that the typical ΣSFR/M_halo for our PACS clusters is largely consistent with that in the field, as represented by the global relation, over the redshift range probed here. Our clusters are also consistent with the redshift evolution of the ΣSFR/M${}_{{\rm{halo}}}-z$ relation found by Geach et al. (2006) and Webb et al. (2013); however, they are inconsistent with SF being suppressed below the global field SFR in ≳10¹⁴ ${M}_{\odot }$ halos at all redshifts as suggested in some studies (i.e., Popesso et al. 2015).

Lastly, in Figure 9, we show the evolution of the mass-normalized integrated SFR for the all ISCS clusters from z = 0.5–1.5. Using the average SFRs derived from stacking on Herschel/SPIRE imaging and assuming a typical halo mass of $8\times {10}^{13}\;{M}_{\odot }$ that is constant with redshift (see Alberts et al. 2014 for details), we find a best-fit function of ΣSFR/M_halo ∼ (1 + z)^{4.9 ± 1.1} for mass-limited cluster galaxy samples from the full ISCS. The redshift evolution of the ISCS clusters, as represented by the shape and slope of this function, is therefore in good agreement with the redshift evolution up to z ∼ 1 as measured in Webb et al. (2013), despite differences in cluster and galaxy selection. On the other hand, we see a significant disparity in the amplitude of ΣSFR/M_halo for the full ISCS sample, which falls well above that measured for the Webb et al. (2013) clusters and our PACS-selected cluster sample. We reiterate that we have made a simplifying assumption by adopting the median mass of the ISCS as measured statistically in Alberts et al. (2014) in this analysis, which will affect the normalization. Keeping this assumption in mind, we attribute this difference in normalization to two main factors. The first is cluster selection: the ISCS clusters are selected in the rest-frame NIR, which probes the in situ stellar mass content, whereas the Red Sequence Cluster (RCS; Gladders & Yee 2005) sample used in Webb et al. (2013) were selected using an observable that depends on a clusters SF history. Specifically, at fixed mass, the RCS is more sensitive to clusters with higher passive galaxy fraction, and correspondingly more prominent and tighter red sequences. This may, in part, explain why the overall level of SF is lower in the RCS sample. The difference between the full ISCS sample and the ISCS/IDCS clusters observed with PACS, however, suggests a second factor, the halo mass, plays at least a partial role. The ΣSFR/M_halo–M_halo relation in clusters is still uncertain. Current estimates suggest a strong link between SF in clusters and halo mass, with Webb et al. (2013) finding ΣSFR/M_halo ∼ M_halo^{−1.5 ± 0.4} for clusters up to z ∼ 1 using richness as a proxy for halo mass (see also Popesso et al. 2015). The difference in amplitude between the full ISCS sample and the higher mass PACS-selected subsample is broadly consistent with this relation (though see above for a discussion on the cluster-to-cluster variation in SF activity relative to cluster richness for the PACS-selected sample). A large uniformly selected cluster survey with well characterized halo masses and SF activity is needed to further constrain the ΣSFR/M_halo–M_halo relation.

Studies of X-ray-, MIR-, and radio-selected AGNs in the ISCS cluster sample have established that the AGN fraction increases dramatically within cluster environments to high redshift, climbing to field-like AGN fractions at z ∼ 1.25 (Galametz et al. 2009; Martini et al. 2009, 2013), a 2 orders of magnitude increase over local clusters. In this section, we examine the evolution of the AGN fraction in clusters selected through SED fitting as a function of redshift and radius. Using SED fitting allows us to identify lower luminosity AGNs than can be selected through shallow to moderate depth X-ray/radio observations or MIR color diagnostics (see Figure 1). To avoid the bias against luminous AGNs introduced by requiring photometric redshifts for cluster membership, for this analysis we opt to do a line of sight study in order to isolate cluster trends. We therefore analyze all galaxies along the line of sight to our clusters regardless of the photometric redshift of the galaxy. To increase our statistical power and take advantage of all available data, we expand this analysis to include ∼250 ISCS plus 3 IDCS clusters between 0.5 < z < 2.

We again divide the galaxies in the photometric redshift catalog into four categories: F_gal < 0.3 ("AGN-dominated"), 0.3 < F_gal < 0.5 ("AGN-composite"), 0.5 < F_gal < 0.7 ("host-composite"), and F_gal > 0.7 ("host-dominated"). Note that the F_gal parameter is not sensitive to SF activity, so these categories should not be interpreted as SFGs versus AGNs, but rather by a decreasing degree of AGN influence on the UV-MIR SED of all galaxy types, including non-star-forming ellipticals.

Figure 10 shows the weighted average of the fraction of each category along the line of sight to the ISCS/IDCS cluster cores (r < 0.5 Mpc) as a function of redshift. Host-dominated galaxies dominate the numbers; however, we find a marked decrease in this subtype with increasing redshift, from 65% to 48% from z = 0.5 to z = 1.5. The bulk of this difference is countered by an increase in host-composite galaxies, with smaller gains in the AGN-composites and AGN-dominated galaxies.

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Next we examine the field-relative fraction of each galaxy subtype as a function of cluster-centric radius in order to isolate how much of the evolving fraction is due to the cluster environment. We bin our clusters into three redshift bins: 0.5 < z < 1 (146 clusters), 1 < z < 1.5 (80 clusters), and 1.5 < z < 2 (22 clusters). We then quantify the fraction of each subtype along the line of sight in radial bins, out to projected radii of 3 Mpc, which is taken to be the field value (Figure 11). We adopt 3 Mpc rather than the 2 Mpc used in the SF analysis as we are not limited by the PACS footprint and therefore can go to larger radii to look for variations in the cluster outskirts. We verify that normalizing at 3 Mpc over 2 Mpc does not change our results, nor does using cumulative or differential annuli, which indicates that there is no significant variation beyond 1 Mpc due to environment. In the lowest redshift bin, host-dominated galaxies are overrepresented in clusters at ∼110% of the field value, with host/AGN-composites and AGN-dominated galaxies underrepresented by ∼10%–30%. By z = 1, host-dominated galaxies have dropped below the field level, with host-composites slightly above and AGN-composites rising to ∼130% of the field level. The fraction of AGN-dominated galaxies has also risen, though it is still below the field value. For our highest redshift clusters, however, AGN-dominated, AGN-composites, and host-composites are all above the field, with AGN-composites at 150% of the field level.

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These results show a substantial rise in the fraction of AGNs and AGN-composites in the cluster cores, consistent with previous studies (Galametz et al. 2009; Martini et al. 2009, 2013). In addition, we demonstrate a rise in AGN-composite galaxies, representing relatively weak AGNs and/or strong host galaxies, and a decline in host-dominated sources with <30% contribution to their UV-MIR SED from AGN emission. The implications of this and how it relates to the observed increase in SF with redshift is discussed in Section 4.

Due to observational challenges and the increasing scarcity of massive clusters at high redshifts, detailed studies of cluster populations during this epoch are typically performed on individual clusters. It is therefore important to understand the range in variation from cluster to cluster in the high-redshift (z > 1) regime. In this work, we have analyzed a statistical sample of uniformly selected clusters with similar halo masses over a relatively small redshift range using self-consistent techniques for identifying cluster membership and measuring cluster galaxy properties. We find a significant scatter in the total dust-obscured SF activity across our cluster sample. This is best seen in Figure 8, where the total SFR per area covers a large range, with greater variation in the cluster cores than in the outskirts (1 < r/Mpc < 2). Strong variations in the mass-normalized integrated SFRs of massive clusters at z ∼ 0.5 were first noted in Geach et al. (2006); we similarly find a wide range in ΣSFR/M_halo for our cluster sample. This scatter in the SF activity at roughly fixed halo mass introduces the possibility of significant bias in single cluster studies. Differences in the SF activity of cluster populations is likely due to a variety of factors, including differences in assembly history and dynamical state. As noted in Muzzin et al. (2012), even relatively evolved rich clusters at z ∼ 1 show a wide range of morphologies. The X-ray observations available for a few clusters (ID8, ID10, ID11) in our sample indicate that these clusters are unrelaxed, with associated filamentary structures, and, in one case, may have undergone a recent interaction or merging event (Brodwin et al. 2011, 2016). As clusters at higher redshift are more likely to have recently experienced evolution in their dynamical state, it becomes more and more important to work with statistical cluster samples which can be used to average over these variations.

Of particular note in our sample are the clusters ID6 (z = 1.396) and ID10 (z = 1.487). As described in Section 2.7, these two clusters have a small angular separation (∼4' or ∼2 Mpc at z ∼ 1.4), and, given their overlap both spatially and in photometric redshift space, we have assured no double counting of galaxies by assigning membership based on the maximum integrated PDF of the photometric redshifts. As a system and individually these two clusters stand out among our sample, with ΣSFR per area two to nine times larger within the virial radius than in the surrounding outskirts. ID6 and ID10 have the highest fractions of PACS-detected, IR-luminous galaxies within 250 kpc in our sample, ∼60% and 50%, respectively, relative to a median fraction of ∼20% for the full cluster sample. Interestingly, ID10 also has nearly half of its SF coming from galaxies below our PACS detection limit, as determined through stacking. Evidence suggests that these lower SFR galaxies may be undergoing enhancement by the cluster environment (see Sections 3.2.1 and 3.2.3). Though their separation in redshift space (nearly 200 Mpc, comoving line of sight) makes it unlikely that these clusters are currently merging, we speculate their substantial SF may be related to their connection within large-scale structure. If so, then these clusters represent an important example of the effects of the dynamical state on cluster galaxies at high redshift, complimentary to lower redshift studies of more extreme merging cluster systems (e.g., Clowe et al. 2004; Chung et al. 2010; Menanteau et al. 2012). Spectroscopic follow up of this system to more accurately map cluster membership and localize SF to the clusters and any filamentary structure in between would provide important constraints on the hierarchical growth of clusters at high redshift.

We also highlight ID11, the most massive cluster known at z > 1.5. ID11 (Stanford et al. 2012) is detected in both SZ (Brodwin et al. 2012) and X-ray (Brodwin et al. 2016), with a consistent ICM-based mass of ${{\rm{M}}}_{200}\approx 4\times {10}^{14}\;{M}_{\odot }$ (see also Mo et al. 2016 for a weak lensing analysis). As demonstrated in Brodwin et al. (2012), given its mass and redshift (z = 1.75), ID11 is a true predecessor to Coma-like clusters, the most massive virialized structures in the local universe. It is therefore unique within our cluster sample. Recently, deep X-ray measurements have also determined that ID11 is possibly a cool-core cluster that has experienced a recent interaction or merger (Brodwin et al. 2016).

As discussed in Brodwin et al. (2013), the transition epoch from active SF to efficient quenching is predicted to occur earlier for more massive systems. Evidence for such cluster downsizing (e.g., Neistein et al. 2006) was presented in Wylezalek et al. (2014), which looked at the MIR luminosity function of cluster candidates around radio-loud AGNs at z ∼ 1–3. In contrast with the Mancone et al. (2010) results discussed earlier, they found that the luminosity function of cluster candidate galaxies were consistent with passive evolution models and high formation redshifts (${z}_{f}\sim 3$). Given that radio-loud AGNs are expected to reside in extremely massive halos, these two studies are consistent within the framework of cluster downsizing. Further corroborating this finding, Welikala et al. (2016) recently found significant SF in the environments of ∼10¹³ ${M}_{\odot }$ halos at z ∼ 1, in sharp contrast with the quenched populations of more massive clusters at the same redshift (e.g., Patel et al. 2009; Brodwin et al. 2013; Alberts et al. 2014). We therefore might expect ID11 to have undergone an earlier transition epoch and to be relatively evolved, given its redshift and mass. Indeed, we find that ID11 has little SF activity in its core (∼15% detection fraction with PACS at <250 kpc) and a low mass-normalized SFR (Figure 9) compared to the rest of our sample and the global field relation, indicating it is already an evolved system. We note, however, that there is a similar mass cluster known at z = 1.58 that shows substantial ongoing SF activity (Santos et al. 2015; Tozzi et al. 2015), again stressing that the scatter due to cluster-to-cluster variations limits what we can conclude based on individual clusters.

To first approximation, the co-evolution of SF and BH growth (i.e., AGN activity) seems unsurprising, given that both processes are driven by the availability of the same cold gas supply. The disparate size scales, however, with SF occurring throughout in the disk and AGN growth at sub-kpc scales, make establishing a link between these two processes challenging (see Alexander & Hickox 2012; Brandt & Alexander 2015 for reviews). Simulations find that the physical processes that feed BH growth on small spatial scales are unlikely to be smooth or continuous, leading them to vary dramatically on short timescales (Hopkins & Quataert 2010; Hickox et al. 2014). This variation may hide a strong underlying correlation with longer-lived SF activity (e.g., Gabor & Bournaud 2013; Hickox et al. 2014; Neistein & Netzer 2014; Thacker et al. 2014; Delvecchio et al. 2015; Xu et al. 2015). The relation between average BH growth and global SFRs, combined with the parallel redshift evolution of SF and AGN activity (see Madau & Dickinson 2014, for a review) and possible observations of an AGN MS (Mullaney et al. 2012b), suggest an important link, the nature of which is still heavily debated. AGN can be triggered by either internal secular evolution processes—disk instabilities, bars, etc.—or through galaxy interactions such as harassment and minor and major mergers (see Alexander & Hickox 2012; Brandt & Alexander 2015, for a review), which may be an important mechanism for BH growth in overdense environments (e.g., Brodwin et al. 2013; Ehlert et al. 2015).

In this work, we looked at the field-relative fraction of AGN-dominated and AGN-composite cluster galaxies as a function of redshift in a sample of ∼250 ISCS/IDCS clusters from 0.5 < z < 2 (Figure 11). We found that the fraction of AGN, as selected through SED fitting, increases steeply in cluster cores with increasing redshift, consistent with previous studies of luminous AGN (e.g., Galametz et al. 2009; Martini et al. 2013). The fraction of AGN-dominated and AGN-composite cluster galaxies is below the field level at z < 1, consistent with previous studies (e.g., Galametz et al. 2009; Ehlert et al. 2013, 2014). At z = 1–1.5, AGN-dominated cluster galaxies increase to just below field level, while AGN-composites are found in excess of the field. This rise in the AGN-composite fraction indicates that the cluster environment stimulates moderate BH growth in addition to the luminous AGN systems previously indicated by X-ray, radio, and MIR selections. By z > 1.5, both AGN-dominated and AGN-composite systems are found in excess of the field level, implying that the environment is triggering AGN over a range of luminosities through increased galaxy interactions in ∼10¹⁴ ${M}_{\odot }$ halos. The rapid decline in excess AGN-dominated galaxies by z ∼ 1–1.5 suggests a transition toward fewer galaxy interactions as the cluster virializes and cluster velocity dispersions increase. This transition epoch is remarkably similar to the behavior we see for the evolution of SF activity in cluster galaxies, wherein SF quenching becomes effective around the same epoch. AGN could provide a mechanism for quenching SF by heating and/or expelling cold gas. In addition to permitting a rapid evolution of cluster galaxies onto the red sequence (see Brodwin et al. 2013), this feedback would also lead to the suppression of AGN activity in clusters at lower redshifts.

SF in cluster galaxies can be influenced by multiple mechanisms specific to overdense environments, the efficiencies of which likely vary as clusters grow in mass and/or undergo virialization. Some of the commonly invoked mechanisms include strangulation (Larson et al. 1980)—the removal of loosely bound hot halo gas by the ICM on long timescales—ram pressure stripping (RPS; Gunn & Gott 1972)—the removal of the interstellar medium through interactions with the ICM on moderate timescales—and galaxy interactions such as harassment (Moore et al. 1996) and/or mergers. For an overview of these processes, see Boselli & Gavazzi (2006).

Recently, studies have revealed that galaxies in the field at high redshift (z = 1–3) have short gas depletion timescales (∼0.7 Gyr; Daddi et al. 2010; Tacconi et al. 2010; Magnelli et al. 2013), stressing the connection between new gas accretion and prolonged SF. This has important implications for overdense environments where the hot ICM in clusters will suppress new gas accretion from cold flows (e.g., Kereš et al. 2005) and where processes such as strangulation and RPS may limit available gas supplies on distinct timescales.

In Section 3.2.3, we found that IR-luminous cluster members are the typical SFGs in our clusters, accounting for the bulk of cluster SF. We consider this result in conjunction with (1) the flat $\langle {\rm{SFR}}\rangle$ of IR-luminous cluster galaxies as a function of cluster-centric radius (Figure 5) for both of our redshift bins, and (2) our stacking analysis (Figure 7), which shows a marked decrease in the $\langle {\rm{SFR}}\rangle$ at small projected radii at z < 1.37 for stellar mass-limited cluster samples. Taken together, these lines of evidence indicate that these clusters are undergoing a rapid build up of the red sequence, with SFGs experiencing significant quenching over short timescales (the period spanning the median redshifts of our bins, z = 1.46 to z = 1.26, is ∼0.6 Gyr), rather than a gradual decrease in the SFRs of cluster SFGs over long timescales. Further constraining the timescale to fully quench cluster galaxies will require measurements of the SF histories in individual cluster galaxies, both star-forming and quenched, over a long redshift baseline.

Strangulation typically occurs over several gigayears, too long to cause the transition seen in the star-forming fraction of cluster galaxies at z ∼ 1.4, indicating that it is not dominating quenching in high redshift clusters. RPS is a more rapid process, expected to act on a timescale less than or similar to the crossing time (∼1–2 Gyr for our cluster sample, assuming velocity dispersions of 700 km s⁻¹; Brodwin et al. 2011). However, though examples of efficient RPS have been observed in massive clusters locally (e.g., Ebeling et al. 2014), RPS fails to explain the enhanced SF in lower mass galaxies and excess AGN fraction in the cluster cores that we find in this work as well as the rapid reddening of cluster galaxies colors by z ∼ 1 (Eisenhardt et al. 2008). As described in Alberts et al. (2014), however, we found that quenching continues at later epochs (z < 1) at rates faster than in the field, with an average timescale consistent with strangulation and RPS, and we posit that these processes may also be related to the decline in AGN activity below what is observed in the field (e.g., Galametz et al. 2009; this work). A vital next step in evaluating the evolution of cluster populations from low to high redshift is directly observing the ISM, particularly the molecular gas supply, in cluster galaxies in relation to SF and AGN activity. Measurements of the mass of the ISM in cluster galaxies at z = 1.75 will be presented in future work (Alberts et al. 2016, in preparation).

There is growing evidence that galaxy interactions, specifically mergers, play a dominant role in clusters at high redshift, where the conditions for galaxy interactions are favorable due to high galaxy space densities and low relative velocities (∼700 km s⁻¹ in the ISCS clusters; Brodwin et al. 2011). For example, rapid mass growth in cluster galaxies at z ≳ 1.3 (e.g., Mancone et al. 2010, 2012) and a dearth of massive red sequence galaxies at high redshift (e.g., Fassbender et al. 2011; Mancone et al. 2012; Rudnick et al. 2012) provide statistical evidence of merger activity. An enhanced merger fraction has been observed in a cluster at z = 1.62 (Lotz et al. 2013), and cluster ETGs at z > 1 have been found to have stochastic SF histories (Snyder et al. 2012) and residual SF (Mei et al. 2015; Wagner et al. 2015), indicating that they recently and rapidly quenched. A significant fraction of cluster SFGs and ETGs have been observed to have disturbed morphologies in z > 1.5 clusters (Mei et al. 2015; Santos et al. 2015). Minor and/or dry mergers may additionally explain the larger size of quiescent galaxies in clusters at high redshift relative to the field (Papovich et al. 2012; Bassett et al. 2013; Delaye et al. 2014; Strazzullo et al. 2015).

In this work, we have demonstrated an excess in the AGN fraction, which we attribute to increased galaxy interactions, such as mergers. In addition, we find that the average SSFR of IR-luminous cluster galaxies at z ≲ 1.4 is suppressed in the cluster cores, while their average SFRs remain constant. This suggests mass build up which occurs without significantly altering SFRs, either through enhancement or quenching, which may indicate dry merger activity. Together with previous studies, these lines of evidence indicate an enhanced merger rate. For further discussion of the role of mergers and AGNs in quenching SF in high-redshift clusters, see Brodwin et al. (2013).