UV TO FAR-IR CATALOG OF A GALAXY SAMPLE IN NEARBY CLUSTERS: SPECTRAL ENERGY DISTRIBUTIONS AND ENVIRONMENTAL TRENDS

The clusters of galaxies are excellent laboratories for studying the influence of the environment on galaxies. This influence is formed by environmental processes which are combinations of interactions of galaxies with other components of the universe: galaxies, dark matter, and plasma. The highest peaks of density in the spatial distribution of these components are in the cores of galaxy clusters. The galaxy population in the centers of clusters reaches volume densities of up to 10³ bright galaxies per Mpc³ on spatial scales of ∼1 Mpc, and those galaxies have relative velocities of several hundreds of km s⁻¹ (Cox 2000). The mass of dark matter halos of clusters is several orders of magnitude greater than the sum of masses of the stellar component of galaxies with mass-to-light ratios that range from 100 to 500 M_☉/L_☉ (Cox 2000) opposite the mass-to-light ratio for stellar component which covers the range 1–10 M_☉/L_☉ (Bell et al. 2003). The pressure of the intracluster medium (ICM), which with n_e  ∼ 10⁻³ cm⁻³ and temperatures from 10⁷ to 10⁸ K, is nearly high enough to act on the gas component of galaxies (Gunn & Gott 1972).

Each of the interactions of galaxies with these components (galaxies, ICM, dark matter halo) has a contribution in the different environmental processes. The interaction of galaxies with the ICM dominates the gas stripping processes, where the interstellar medium of galaxies is stripped via various mechanisms, including viscous and turbulent stripping (Toniazzo & Schindler 2001), thermal evaporation (Cowie & Songaila 1977), and ram pressure stripping (Quilis et al. 2000). The tidal interactions among galaxies dominate the galaxy mergers or strong galaxy–galaxy interactions (Mihos 2004) and the galaxy harassment (Moore et al. 1996, 1998, 1999). The environmental process known as strangulation, starvation, or suffocation is dominated by the tidal interaction with the dark matter halo of the cluster which removes the hot gas halo of the galaxy (Bekki et al. 2002). The environmental processes act on the stellar and gas/dust components of a galaxy modifying its gas content, the star formation level, the structural and dynamical parameters, etc. On the one hand, the intensity of the environmental processes depends on galaxy properties such as the stellar mass or the compactness of stellar component. Also, the environmental influence depends on the environmental conditions and/or the cluster properties as the density of cluster components (galaxies, ICM, dark matter halo), the velocity field of the cluster, etc. Specifically, there is a controversy about the dependence on global cluster properties (e.g., σ_c) of the star formation activity of cluster galaxy population. Numerous works point out that there is no such correlation (Smail et al. 1998; Andreon & Ettori 1999; Ellingson et al. 2001; Fairley et al. 2002; De Propris et al. 2004; Goto 2005; Wilman et al. 2005; Andreon et al. 2006) while other works claim the presence of a relation between the star formation activity and the global cluster properties (Martínez et al. 2002; Biviano et al. 1997; Zabludoff & Mulchaey 1998; Margoniner et al. 2001).

The cores of galaxy cluster are located around the peaks of densities of these components but the volume density of cluster components converges to the field value toward regions outside the virial regions in distances of some virial radii (Cox 2000; Rines et al. 2003). So, the transition between the cluster centers and the surroundings samples a broad range of environmental properties. The environmental processes act on galaxies with different intensity depending on the galaxy (dynamical or stellar) mass or luminosity (see Boselli & Gavazzi 2006, for a review), but in most of the previous works the observed trends of galaxy properties are restricted to giant L  ≳  L* galaxies. The UV luminosity has revealed as a good proxy of the recent star formation rate (SFR) because it is a tracer of the more short-lived stars τ  < 10⁸ yr (Kennicutt 1998) and the UV–optical colors as an excellent classifier between passive evolving galaxies and star-forming galaxies (Chilingarian & Zolotukhin 2012). On the other hand, the optical and near-infrared spectral ranges sample stellar populations with ages that range from 10⁹ to 10¹⁰ years (Kennicutt 1998; Martin et al. 2005). This provides some important insights into the global star formation history of a galaxy, i.e., the stellar mass, the timescale of the star formation history, etc.

Following the former considerations, we will design a sample of clusters nearly nearby enough for their galaxies to be observed around the classical luminosity limit between giant and dwarf galaxies M_B = −18 by the DR6 of Sloan Digital Sky Survey (SDSS). We stress that the cluster galaxy population must be observed by different surveys from UV to Far-IR (FIR) in the central regions of each cluster and its surroundings up to several times the size of virial region. This cluster sample allows us to study the environmental behavior of different properties (current star formation, stellar mass, attenuation, etc.) of a galaxy population with a broad luminosity range that inhabits environments as different as the center of galaxy clusters or their surroundings.

The remainder of the paper is organized as follows. In Section 2, we describe the design of the cluster sample. In Section 3, we describe the compilation of broadband and emission line fluxes for the galaxy sample of the cluster sample. In Section 4, we show the compilation of fluxes for the galaxy sample, color–color distributions, and an example of the spectral energy distribution (SED) of a galaxy from the sample. In Section 5, we discussed three different items: the bolometric X-ray luminosity versus cluster velocity dispersion L_X–σ_c relation, the local density Σ₅ distribution of galaxy population split by their membership to virial regions of low-mass/massive clusters, and as a hint for future work and the sky projected density of giant/low-luminosity and passive/star-forming galaxy population in a massive cluster. We summarized our findings in Section 6.

One of the purposes of the sample design is embrace a luminosity range for the cluster galaxy sample wide enough to contain the classical limit between giant and dwarf galaxies, M_B = −18. This constrains the redshift range of the cluster sample. The cluster sample is observed in a sky area which is delimited by the intersection of observed sky areas of SDSS and Galaxy Evolution Explorer (GALEX) surveys. Both surveys are incomplete (at the moment of sample definition, 2008 March) and have smaller observed sky areas than the other ended surveys, Two Micron All Sky Survey (2MASS) and Infrared Astronomical Satellite (IRAS). In order to sample a broad range of environments, we select galaxy clusters observed by these surveys up to regions several virial radius beyond the virial region. So, we discard those clusters with a poor sky coverage not only in the central regions but also in the outskirts of clusters.

In the following, we describe the process of building the cluster sample. In a first step, we take a compilation of Galaxy Clusters from NED.³ Thanks to this approach, we take account of all cluster selection criteria in the literature; visual inspection, image-smoothing techniques, X-ray extended sources detection, Red Sequence algorithm, surveys around cD galaxies, etc. This avoids any kind of bias in the cluster selection. We have selected all astrophysical objects with NED Object Type set to GClstr.

We constrain the redshift range to reach down to the absolute magnitude limit of dwarf galaxies. The Main Galaxy Sample of SDSS reaches up to r'_MGS = 17.77 (Strauss et al. 2002), while the absolute magnitude limit for a dwarf galaxy starts at M^Dwarf_B = −18 (Binggeli et al. 1988; Mateo 1998), so

$$\begin{equation*} \left\lbrace \begin{array}{@{}l} R = r^{\prime }_{{\rm MGS}} - 0.1837(g-r) - 0.0971 \;\hbox{({Lupton} 2005)} \\\ms M_{R} \equiv M_{B}^{{\rm Dwarf}} - (B-R) \\\ms \mu \equiv R - M_{R} \\\ms \log {\it {z}} = 0.2 \mu - 8.477 + \log {\it {h}} \hbox{(Local Universe,}\\ \quad\quad\quad\hbox{i.e., c{\it {z}} $\approx $ H{\it {D}} = 100{\it h}{\it {D}}),} \end{array} \right. \end{equation*}$$

with B, R apparent Johnson magnitudes; M_B, M_R absolute Johnson magnitudes; μ distance modulus; H  ≡ 100h with H the Hubble's constant and z the redshift. We assume h = 0.7 in this work. Assuming the (B − R) values observed by Mobasher et al. (2003) and the (g − r) values observed by Blanton et al. (2003) for red and blue galaxies, we obtain an upper limit in redshift of

$$\begin{eqnarray*} &&\left\lbrace \begin{array}{@{}l}\hbox{Blue galaxies: }(B-R)\approx 0.8, \; ({g-r})\approx 0.2 \Rightarrow {\it {z}}\approx 0.044 \nonumber \\\ms \hbox{Red galaxies: } (B-R)\approx 2.0, \; ({g-r})\approx 1.0 \Rightarrow {\it {z}}\approx 0.071. \end{array} \right. \end{eqnarray*}$$

Then, we choose z = 0.05 as the upper limit in redshift as a compromise between red and blue galaxies and initially start with a cluster sample from z = 0 to z = 0.05. This initial sample contains 1575 clusters.

We check by eye the distribution of the SDSS plates and the GALEX fields for this cluster sample over a sky region up to a projected radius of some Abell radius (Abell 1958) from the center of each cluster. After that we set the lower limit in redshift for the cluster sample to z = 0.02 because this redshift limit is enough to cover the sky area of a typical galaxy cluster with only a few SDSS plates (15 radius) or GALEX fields (05 radius). In a second step, we cross-correlated the coordinates of cluster centers reported by NED with the position of the SDSS plates and the GALEX fields, in order to know what the cluster centers are, at least, in one SDSS plate and one GALEX field. This gives a cluster sample of 373 galaxy clusters with redshift from 0.02 to 0.05. In order to get a good SDSS sky coverage of clusters, we check by eye the sky coverage of SDSS Main Galaxy Sample up to a projected radius of 2.2 R_Abell from the cluster center. For a subsequent procedure, we need a spectroscopic galaxy sample covering a sky region either with this specific radius or more extensive. We select only those clusters with a good SDSS sky coverage over a sky area with this size. This selection gives a sample of 230 clusters for 0.02 < z  < 0.05.

The clusters from different catalogs have different selection and detection criteria and we do not control whether there are spurious clusters in some of these catalogs. On the one hand, we have to clean our cluster sample of non-confident clusters and possible artifacts. On the other hand, we need a reliable measure of the cluster velocity dispersion, σ_c, in order to characterize a cluster sample with a broad range in σ_c, from poor to rich clusters. We solve these two issues using the procedure proposed by Poggianti et al. (2006) in their Appendix C but assuming a cluster center reported by NED instead of Bright Cluster Galaxy as the center of the galaxy cluster.

In the first step, we select the galaxies inside 2.2 Abell radii from the NED center and within a redshift range defined by Δz = ±0.015 from the cluster redshift given by NED. From these galaxies, we estimate the cluster redshift z_c and the cluster redshift dispersion σ_z as the median and the median absolute deviation, respectively. If σ_z is higher than 0.0017 (≈σ_c = 500 km s⁻¹ at z = 0), we set σ_z to this value. This step is useful for avoiding too much contamination from surrounding galaxy structures. Then, we computed the radius r₂₀₀ from z_c and σ_z using the following equation:

$$\begin{equation} r_{200} = 1.73 \frac{\sigma _{c}}{1000\ {\rm km\ s^{-1}}} \frac{1}{\sqrt{\Omega _{\lambda } + \Omega _{0}(1+z_{c})^{3} }}\ h^{-1}\ {\rm Mpc}, \end{equation} \tag{ 1 }$$

which is taken from Finn et al. (2005). First, we recomputed z_c and then σ_z from those galaxies within ±3σ_z from z_c and nearer to the cluster center than 1.2 r₂₀₀. This process iterates until it reaches the convergence. After each iteration, every galaxy in the initial sample can reenter the cluster sample whether it meets the constraints on redshift and position or not. If the process does not converge, we discard that cluster. The error of the final σ_c is computed using a bootstrap algorithm that applies to the galaxy sample in each cluster.

In this procedure, there are clusters which reach the convergence and show a final z_c far away from NED cluster redshift or with σ_z ≫ 1000 km s⁻¹. After a visual check of radial velocity histograms of these structures, we conclude that those galaxy structures are far from being real clusters. In order to discard those structures, we add two constraints to the cluster sample:

$$\begin{eqnarray*} &&|z_{c} - z_{{\rm NED}}| \le 0.0033 \nonumber \\ &&\sigma _{z} \le 1300 \ {\rm km\ s^{-1}}. \nonumber \end{eqnarray*}$$

After applying the procedure from Poggianti et al. (2006) and including this constraint to the former sample, the resulting sample is composed of 86 clusters. At the end of this procedure, we still impose a further condition related to the presence of clusters with more than one NED identifier: NED only classifies two clusters from different catalogs as being the same cluster if their angular separation is less than 2 arcmin (Marion Schmitz–NED team, 2008, private communication). Using this clue, we take the cluster name from the most ancient catalog to identify those clusters with more than one NED identifier.

As a final step, we visually check the GALEX AIS coverage of each cluster up to some Abell radius. We end up with 16 clusters in the redshift range 0.02 < z  < 0.05. Their basic properties are listed in Table 1. Their appearance in the sky and their radial velocity distributions are shown in Figures 1 and 2, respectively. Figure 3 shows the color-composite images of the central regions of clusters retrieved from the SDSS Navigate Tool http://skyserver.sdss.org/public/en/tools/chart/navi.asp.

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Taking a look to the sky distribution of clusters from the sample in Figure 1, one can see the wide variety of the cluster sample in cluster richness and spatial structure and in some cases, the presence of galaxy structures around the virial regions of clusters. The richness ranges from the poor cluster WBL 245 or WBL 234 with only a few galaxies in their central regions, to the massive cluster ABELL 2199, which is assembled in the supercluster ABELL 2197–ABELL 2199–B2 1621+38:[MLO2002] or the cluster ABELL 1185, with clear evidence of galaxy structures as filaments. There are apparently "isolated" clusters as UGCl 271 or UGCl 148 NED01 opposite the example of WBL 514 with a close "twin" cluster, WBL 518 (Beers et al. 1995).

One purpose of this work is the compilation of broadband and emission line fluxes from the ultraviolet around 1350 Å to the far-infrared around 100 μm for the cluster galaxy sample. In the last few decades, this task has become possible thanks to several sky surveys covering large areas of the sky from UV to FIR. We present a brief summary of the main galaxy surveys from which we retrieve spectrophotometric fluxes for the cluster galaxy sample and summarize the main figures of each survey in Table 2.

• 1.  
  Galaxy Evolution Explorer (GALEX; Martin et al. 2005) was launched to, among other surveys, cover all sky at different depth and areas in two UV filters, the far-ultraviolet (FUV) band (1350–1750 Å) and the near-ultraviolet (NUV) band (1750–2750 Å). The AIS plans to survey the entire sky down to a sensitivity of m_AB  ≈ 20.5, comparable with the sensitivity of the SDSS Main Galaxy Sample, r'_MGS = 17.77 (Strauss et al. 2002).
• 2.  
  The SDSS Project (6th Data Release in Adelman-McCarthy et al. 2008) retrieved spectra from, among other astronomical objects, all galaxies with r'  < 17.77 from the SDSS Imaging Catalog. The SDSS photometric system (Fukugita et al. 1996) covers from 3000 to 11000 Å in five broadband filters (u', g', r', i', and z').
• 3.  
  The 2MASS (Cutri et al. 2001) has uniformly scanned the majority of the sky in three near-infrared (NIR) bands, J (1.25 μm), H (1.65 μm), and K_s (2.17 μm).
• 4.  
  The IRAS (Neugebauer et al. 1984) was a project to perform an unbiased, sensitive all sky survey at 12, 25, 60, and 100 μm, down to a limiting flux of 0.2 Jy at 60 μm. This mission produced two main catalogs: the Point Source Catalog (PSC; Joint Iras Science 1994) and the Faint Source Catalog (FSC; Moshir et al. 1993).

3.1. SDSS Data

The cross-correlation of celestial coordinates from different catalogs has been accomplished using the SDSS celestial coordinates as the fiducial coordinates. For each cluster, we retrieve all galaxies from the DR6 of SDSS with the following criteria:

• 1.  
  R_P ⩽ 7.1 Mpc
• 2.  
  z_c − 5σ_c ⩽ z ⩽ z_c + 5σ_c
• 3.  
  z ⩾ 10⁻³ (in order to avoid stars in the lowest redshift clusters).

We retrieve photometric and spectroscopic data from the SDSS database for this galaxy sample. The photometric fluxes come from the five broadband filters of SDSS. We select the "composite flux" magnitude (Abazajian et al. 2004) as the suitable way to retrieve the total flux from each galaxy with the minimum uncertainty in color. We summed the error reported by the SDSS photometric pipeline (photo; Lupton et al. 2001) and the calibration errors reported in the DR6 of SDSS (Adelman-McCarthy et al. 2008), the standard deviation (based on the interquartile range) of distribution of the difference r_composite−r_petrosian to account uncertainties in color which are not present in the standard accurate-color photometry (i.e., petrosian magnitude; Abazajian et al. 2004).

We include spectroscopic data regarding to spectroscopic redshift and the fluxes for the four emission lines of the BPT diagram (Baldwin et al. 1981); [O iii] (λ = 5007 Å), Hβ (λ = 4861 Å), [N ii] (λ = 6584 Å), and Hα (λ = 6563 Å). Moustakas et al. (2006) claim that the extinction-corrected Hα luminosity is a reliable SFR tracer, even in highly obscured star-forming galaxies. We derive galaxy SFRs from the extinction-corrected Hα luminosity. The extinction correction is applied using the Balmer decrement method and the Cardelli et al. (1989) extinction law with R_V = 3.1. We take a H i recombination line ratio in the theoretical case B nebulae at T = 10⁴ K as Hα/Hβ = 2.87. We apply the scaling law between SFR and Hα luminosity proposed by Kennicutt (1998).

SDSS project has a pair of fiber-fed double spectrographs with 3 arcsec of fiber diameter on sky. This produces a loss of light from external parts of the largest galaxies. In order to reduce systematic and random errors from this "aperture effect" in SFR estimation, Kewley et al. (2005) recommend selecting galaxy samples with the fiber capturing more than the 20% of the galaxy B_445 nm light. We assume an SDSS spectrum as representative of a galaxy when the fiber contains at least one-fifth of the total g-band flux of the galaxy. So, we select these galaxies with

$$\begin{eqnarray*} &&(g_{{\rm fiber}}-g_{{\rm model}})\le -2.5 \ {\rm a\, log}_{10}(0.2). \end{eqnarray*}$$

g_fiber is the g-band magnitude measured inside an aperture similar to those produced by the SDSS fiber and g_model is the g-band "model" magnitude. In this case, we scale Hα fiber flux to Hα total flux using 10$^{-0.4(g_{{\rm model}}-g_{{\rm fiber}})}$ as scaling factor. Otherwise, we set Hα fiber flux (without any scaling) as the lower limit for the Hα total flux of these galaxies.

3.2. SDSS–GALEX Cross-correlation

3.3. SDSS–2MASS Cross-correlation

3.4. SDSS–IRAS Cross-correlation

The format of the spectrophotometric catalog is presented in Table 3. It contains 53 columns that are described below, including the relevant observational parameters, spectrophotometric fluxes from UV to FIR, and SFR estimates.⁷

Table 3. Spectrophotometric Catalog of Cluster Galaxy Sample

ID	ObjID	SpecObjID	R.A.	Decl.	z	z	ABFUV	σFUV	iFUV	ABNUV	σNUV	iNUV
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
			(deg)	(deg)			(AB mag)	(AB mag)		(AB mag)	(AB mag)
1	587735239565377792	357982217034530816	134.655685	31.482407	0.02662	0.00009	19.049200	0.133216	1	18.683599	0.082258	1
2	587735240639381760	357982219328815104	134.670593	32.448460	0.02233	0.00009	−1.000000	1.000000	−1	−1.000000	1.000000	−1
3	587735043615096960	358263757475938304	135.079346	32.780834	0.02231	0.00009	−1.000000	1.000000	−1	−1.000000	1.000000	−1
4	587735043078946944	358263758667120640	136.960205	33.468132	0.02638	0.00017	21.178200	0.279031	1	19.302299	0.095046	1
5	587735239567474944	358545248592330752	139.522614	33.917965	0.02438	0.00016	−1.000000	1.000000	−1	21.215200	0.279032	1
6	587735239567540352	358545248617496576	139.730331	34.034649	0.02227	0.00018	−1.000000	1.000000	−1	21.621300	0.384734	1
7	587735239567540224	358545248625885184	139.611649	34.036049	0.02317	0.00020	−1.000000	1.000000	−1	21.976801	0.469288	1
8	587735239567737088	358545248667828224	140.089035	34.238449	0.02461	0.00008	−1.000000	1.000000	−1	18.375999	0.050605	1
9	587735239567605888	358545248823017472	139.744232	34.133747	0.02226	0.00015	−1.000000	1.000000	−1	−1.000000	1.000000	−1
10	587735042543124608	358545248831406080	139.641464	34.293934	0.02166	0.00014	−1.000000	1.000000	−1	20.270000	0.189778	1
1	18.517775	0.108053	1	17.700541	0.066168	1	17.592373	0.066663	1	17.379465	0.070090	1	17.194235	0.108876	1
2	19.067827	0.105126	1	18.039906	0.066950	1	17.741817	0.063210	1	17.584496	0.065851	1	17.514194	0.092795	1
3	19.101942	0.120221	1	17.946131	0.064513	1	17.827415	0.065841	1	17.913485	0.068607	1	17.935137	0.115220	1
4	16.447611	0.084428	1	14.826661	0.066261	1	14.076668	0.066026	1	13.676976	0.066025	1	13.416355	0.077062	1
5	17.861683	0.097150	1	16.316339	0.068362	1	15.610915	0.067196	1	15.253843	0.067416	1	15.014357	0.081505	1
6	19.127745	0.143379	1	17.663599	0.073838	1	16.970215	0.070757	1	16.692688	0.071775	1	16.589867	0.093554	1
7	19.035378	0.141855	1	17.719177	0.066303	1	17.065872	0.063679	1	16.616327	0.064424	1	16.577681	0.089601	1
8	17.831083	0.089150	1	16.744196	0.060600	1	16.188751	0.060178	1	16.101572	0.061675	1	16.413498	0.083945	1
9	18.705692	0.112631	1	17.124117	0.071256	1	16.368345	0.071518	1	16.046734	0.069902	1	15.803750	0.090045	1
10	16.759426	0.089591	1	15.192950	0.066689	1	14.493539	0.066356	1	14.086758	0.066258	1	13.676766	0.077599	1
1	16.389999	0.500000	0	16.370001	0.500000	0	16.340000	0.500000	0	10.647425	0.410000	0
2	16.389999	0.500000	0	16.370001	0.500000	0	16.340000	0.500000	0	10.647425	0.410000	0
3	16.389999	0.500000	0	16.370001	0.500000	0	16.340000	0.500000	0	10.647425	0.410000	0
4	13.371000	0.024000	1	13.155000	0.036000	1	13.294000	0.041000	1	10.647425	0.410000	0
5	15.266000	0.056000	1	14.981000	0.064000	1	15.249000	0.089000	1	10.647425	0.410000	0
6	16.389999	0.500000	0	16.370001	0.500000	0	16.340000	0.500000	0	10.647425	0.410000	0
7	16.389999	0.500000	0	16.370001	0.500000	0	16.340000	0.500000	0	10.647425	0.410000	0
8	16.389999	0.500000	0	16.370001	0.500000	0	16.340000	0.500000	0	10.647425	0.410000	0
9	15.926000	0.086000	1	15.654000	0.107000	1	15.919000	0.156000	1	10.647425	0.410000	0
10	13.681000	0.026000	1	13.454000	0.035000	1	13.626000	0.051000	1	10.647425	0.410000	0
1	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.022868	0.012458	−2	U141
2	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.128186	0.052203	1	U141
3	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.208392	0.081589	1	U141
4	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.000000	0.590124	−2	U141
5	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.000000	0.074153	−2	U141
6	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.000000	0.007125	−2	U141
7	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.000000	0.003340	−2	U141
8	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.081989	0.027873	−2	U141
9	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.000000	0.021264	−2	U141
10	10.647425	0.410000	0	10.647425	0.410000	0	8.900000	0.170000	0	0.000000	0.183563	−2	U141

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.

Download table as:  DataTypeset images: 1 2 3 4

Column 1. ID: number associated with the position of the galaxy inside the cluster galaxy sample as an identifier.

Columns 2 and 3. ObjID and specObjID: SDSS Imaging Catalog and Main Galaxy Sample identifier of the galaxy.

Columns 4 and 5. R.A. and decl.: SDSS right ascension and declination (J2000) in degrees.

Columns 6 and 7. z and _z: SDSS spectroscopic redshift and its uncertainty.

In sets of three elements, the following columns show the AB magnitude of galaxy, its uncertainty, and the detection identifier⁸ for the following spectral bands:

Columns 8, 9, and 10. AB_FUV, σ_FUV, and i_FUV: the GALEX FUV band.

Columns 11, 12, and 13. AB_NUV, σ_NUV, and i_NUV: the GALEX NUV band.

Columns 14, 15, and 16. AB$_{u^{\prime }}$, $\sigma _{u^{\prime }}$, and $i_{u^{\prime }}$: the SDSS u' band.

Columns 17, 18, and 19. AB$_{g^{\prime }}$, $\sigma _{g^{\prime }}$, and $i_{g^{\prime }}$: the SDSS g' band.

Columns 20, 21, and 22. AB$_{r^{\prime }}$, $\sigma _{r^{\prime }}$, and $i_{r^{\prime }}$: the SDSS r' band.

Columns 23, 24, and 25. AB$_{i^{\prime }}$, $\sigma _{i^{\prime }}$, and $i_{i^{\prime }}$: the SDSS i' band.

Columns 26, 27, and 28. AB$_{z^{\prime }}$, $\sigma _{z^{\prime }}$, and $i_{z^{\prime }}$: the SDSS z' band.

Columns 29, 30, and 31. AB_J, σ_J, and i_J: the 2MASS J band.

Columns 32, 33, and 34. AB_H, σ_H, and i_H: the 2MASS H band.

Columns 35, 36, and 37. AB_Ks, σ_Ks, and i_Ks: the 2MASS K_s band.

Columns 38, 39, and 40. AB_12 μm, σ_12 μm, and i_12 μm: the IRAS 12 μm band.

Columns 41, 42, and 43. AB_25 μm, σ_25 μm, and i_25 μm: the IRAS 25 μm band.

Columns 44, 45, and 46. AB_60 μm, σ_60 μm, and i_60 μm: the IRAS 60 μm band.

Columns 47, 48, and 49. AB_100 μm, σ_100 μm, and i_100 μm: the IRAS 100 μm band.

Columns 50, 51, and 52. SFR, σ_SFR, and i_SFR: Hα-derived SFR, its uncertainty, and detection identifier in SFR.

Column 53. Cluster: identifier for the parent cluster of the galaxy. The cluster identifiers are codified in the following way: A = ABELL, B2 = B2 1621+38:[MLO2002] CLUSTER, N = NED, U = UGCl, W = WBL.

In Figure 4, we show the cluster galaxy sample in three UV–optical–NIR color–color diagrams; in each panel we show only galaxies with detection in the three corresponding spectral bands. Figure 4 shows how the galaxy sample traces the color distribution of the two main spectral types of galaxies: the passive galaxies and star-forming galaxies. The "red sequence" which is constituted by the family of passive galaxies becomes a "red clump" around (NUV − r) ∼ 5.75, (g − r) ∼ 0.75, and (r − K_s) ∼ 1.0 while the "blue cloud" of the star-forming galaxies turns into a sort of "blue sequence" which is more clearly visible in the UV–optical color diagram. We stress that the spectral information from UV bands allows us a more accurate selection of star-forming galaxies based on UV–optical color diagrams, cf. Section 5.3 and Figure 9. This is especially important for the study of a genuine sample of star-forming galaxies carried out in this and subsequent works.

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Figure 5 shows an example of SED from the cluster galaxy sample composed by the broadband fluxes and the SFR derived from Hα luminosity which covers 3 dex in wavelength and 1 dex in luminosity spectral density. Figure 5 highlights the importance of a consistent photometry capturing the total flux in each band along the SED in order to apply an accurate spectral fitting analysis. Figure 5 also illustrates the comparison of this SED with its best-fitted spectral template from a synthetic spectral library in Hernández-Fernández (2011).

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In this work, we build up an extended catalog of galaxies belonging to a sample of nearby clusters carefully selected to minimize cluster selection bias and to include a large diversity of cluster properties. Special care has been exercised to follow an appropriate methodology producing a self-consistent spectrophotometry along the SED. In this section, we discuss the general properties of the selected clusters, together with the spectral characterization of their galaxies and paying especial attention to the environmental trends of the sample.

5.1. X-Ray Luminosity versus Velocity Dispersion

In Figure 6, we plot bolometric X-ray luminosity versus cluster velocity dispersion, the L_X–σ_c relation, for the cluster sample. The velocity dispersion and the associated errors are computed assuming the procedure proposed by Poggianti et al. (2006). The bolometric X-ray luminosity values are taken from Mahdavi et al. (2000) and Mahdavi & Geller (2001), assigning an uncertainty of 30% to the X-ray luminosity in the same way as Mahdavi & Geller (2001).

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The L_X–σ_c relation for the galaxy clusters with associated X-ray detection (nine clusters) or an associated upper X-ray flux limit (WBL 213) follows in a consistent way the L_X∝σ^4.4_c relation found by Mahdavi & Geller (2001) for a sample of 280 galaxy clusters. For some clusters of the sample, we did not find an associated X-ray source in Mahdavi & Geller (2001) catalog neither one NED object with X-ray associated flux (GGroups, GClusters or Xray source) clearly associated with these clusters. Also, we know there is no sources with X-ray bolometric luminosities under 10⁴¹ erg s⁻¹ in Mahdavi & Geller (2001) catalog. Assuming these clusters are around or under this X-ray luminosity (with the typical uncertainties for these X-ray luminosities) this group would show a locus consistent with L_X∝σ^4.4_c trend, except for the cluster WBL 205. In this cluster, its σ_c is overestimated because WBL 205 is clearly formed by two dynamical substructures (see Figure 2).

5.2. Distribution and Radial Trend of the Local Galaxy Density Σ₅

In Figure 7, we plot the distribution of local galaxy density of the cluster galaxy sample. We choose Σ₅ as local density estimator following Balogh et al. (2004); this density is computed for each galaxy inside a circle containing up to the fifth neighboring galaxies more luminous than M_r = −20.6 with radial velocities not farther than 1000 km s⁻¹ from the radial velocity of each galaxy:

$$\begin{equation} \Sigma _{5} = \frac{5}{4 \pi r^{2}_{5}}, \end{equation} \tag{ 2 }$$

with r₅ the distance to the fifth neighboring galaxy more luminous than M_r = −20.6 within ± 1000 km s⁻¹ in radial velocity. We reject from Σ₅ distributions galaxies with "edge effects," those galaxies in which some of their fifth first neighbors are placed far from the radial limits of galaxy sample (7 Mpc) or with a radial velocity out of the limits given by ±5σ_c around the cluster redshift. We consider four galaxy subsamples in two intervals of velocity dispersion of the parent cluster (σ_c  < 550 km s⁻¹—low-mass clusters and σ_c >550 km s⁻¹—massive clusters) and segregated by their membership to virial regions. The threshold for the cluster velocity dispersion σ_c = 550 km s⁻¹ between the low-mass and the massive clusters approximately matches a gravitational mass of 2×10¹⁴ M_☉ (Cox 2000), a similar value to the characteristic mass of the distribution of cluster mass (Henry & Arnaud 1991). Also, Poggianti et al. (2006) choose a similar value for σ_c as a boundary between two distinct cluster environments with regard to their star formation activity; the massive clusters (those with a high σ_c) are extremely hostile environments for star formation activity. They found a different trend of the [O ii] emission-line fraction with the σ_c in these two cluster environments. The membership to the virial regions is assigned to galaxies inside a projected radius of r₂₀₀ of each cluster and under the general caustic profile in a phase diagram obtained by Rines et al. (2003) for a sample of clusters in the Local Universe.

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In a first look at Figure 7, the Σ₅ ranges from ∼10⁻² to ∼10², a more broad range than the range of Σ₅ distribution shown by Balogh et al. (2004) for two galaxy samples from SDSS DR1 (Sloan Digital Sky Survey Data Release I; Abazajian et al. 2003) and the Σ₅ distribution of 2dFGRS (Two degrees Field Galaxy Redshift Survey; Colless et al. 2001) that range from ∼3×10⁻² to ∼30. In the higher density side, this difference comes from the lower statistics of this two samples (∼186,240 galaxies for SDSS DR1 and ∼220,000 for 2dFGRS) versus the SDSS DR6 with ∼790,220 galaxies, i.e., this release contains a higher number of galaxies from the highest density regions, the clusters.

The Σ₅ distribution of virial regions occupies the range −1  ≲ logΣ₅  ≲ 1.2 in both cases, massive clusters and low-mass clusters. Although the high-density tail of massive clusters (log Σ₅  > 1.2) is absent in the low-mass clusters. In addition, the mean of Σ₅ for massive clusters (log Σ₅  ≈ 0.6) is ≈0.2 dex higher than the mean of Σ₅ for low-mass clusters. We apply a Kolmogorov–Smirnov test to the Σ₅ distributions of virial regions from the low-mass clusters and the massive clusters. They have a probability of ∼4% to come from the same parent population, so they are statistically distinguishable.

The Σ₅ distribution of galaxies from the outskirts presents a common range (−2 ≲ logΣ₅  ≲ 1.3). Further, two differences are noticed: (1) the presence of a high density tail (logΣ₅  ≳ 1.3) in massive clusters and (2) the mean of Σ₅ in the outskirts of massive clusters (log Σ₅  ≈ −0.3) is ≈0.35 dex higher than the corresponding mean for the low-mass clusters.

The difference between the mean of Σ₅ for galaxies in virial regions and galaxies from the outskirts is more than 1 dex for the low-mass clusters versus the difference for massive clusters which is ≈0.9 dex. The overlapping in the high density side of Σ₅ distributions between virial regions and the outskirts can be explained in the following way. The sample is designed following a set of observational constraints described in Section 2, but the galaxy substructures around the virial region of selected clusters in the sample may not fulfill those constraints. So, there may be galaxy structures in the outskirts of virial regions as massive as their parent cluster, the way one would expect from the similarity of the high density tails between virial regions and outskirts. Anyway, there is a Σ₅ interval below logΣ₅  ∼ 1 where the galaxy subsample from the outskirts prevails over the galaxies from virial regions. Also, the absence of the highest density tail in the low-mass clusters is that clear evidence of the local density that reaches its highest value in the more massive galaxy structures, the richest clusters.

In Figure 8, one can see a broad trend for the Σ₅–r_P relation (r_P ≡ R_P/r₂₀₀), with the highest densities near to cluster centers at the top of a correlation in the virial region and the lowest densities far from the virial regions in the same way as found by Rines et al. (2005). We find that the Σ₅–r_P relation is biased in ∼0.5 dex toward lower densities considering the Σ₅–r_P relation obtained by Rines et al. (2005). This bias would come from a deeper luminosity cut for neighboring galaxies which is set to M_K = −22.7, enlarging the sample of neighboring galaxies devoted to compute the local density. The density–radius trend shows a more broad relation outside the virial region than the trend for the virial region. This came from the presence of galaxy structures which have peaks of density similar to those in the center of virial regions (e.g., ABELL 2197 or B2 1621+38:[MLO2002]). The massive clusters show galaxy structures with higher densities in the outskirts of virial regions than the low-mass clusters. Both the massive and low-mass clusters follow a similar trend inside the virial region, but the low-mass clusters reach only up to logΣ₅  ∼ 1.2 avoiding the highest density tail while the massive clusters reach up to logΣ₅  ∼ 2. In the outskirts, the major concentration of galaxies in the lower side of the relation traces a common trend for both massive and low-mass clusters.

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In Figure 8, we plot a King profile (King 1966) fit by eye to the major concentration of galaxy points along the Σ₅–r_p relation:

$$\begin{eqnarray} \log \Sigma &=& \log \Sigma ^{0} - \beta \log \left[ 1 + \left(\frac{r_{p}}{r_{c}} \right)^{2} \right], \hbox{with}\nonumber\\ \Sigma ^{0} &=& 2, \beta =0.75, r_{c}=0.05. \end{eqnarray} \tag{ 3 }$$

The King profile was initially applied to the projected galaxy density of Coma cluster by King (1972). The fit from Equation (3) in the Σ₅–r_p relation seems to reconcile the narrow relation inside the virial region with the concentration in the lower side of the relation for the surroundings. Both the massive and low-mass clusters seem to follow the same relation along the clustercentric radius, with the massive clusters occupying the top of the density–radius fit.

5.3. Galaxy Projected Distribution

In this section, we stress the relevance of a detailed mapping of the sky distribution of different galaxy populations as a tool for the study of environmental trends of galaxy properties. Such study is illustrated here for ABELL 1185, a massive cluster of our sample. A similar analysis extended to the complete cluster sample is out of the scope of this paper and will be presented elsewhere (Hernández-Fernández et al. 2011b). We segregate galaxy populations according to their luminosity between giant galaxies M_r  < −19.5 and low-luminosity galaxies −19.5 < M_r  < −18, and also to their spectral type between passive galaxies and star-forming galaxies. In order to differentiate passive galaxies from star-forming galaxies, we take advantage of the (NUV – r) versus (u – r) color–color diagram. We assume that a galaxy is a passive galaxy whether its colors fulfill the following prescription:

$$\begin{eqnarray*} \left\lbrace \begin{array}{@{}ll}{\rm NUV}-r > 4.9 & \hbox{for }\ {u-r} < 2.175 \nonumber \\ {{\rm NUV}-r} > -2({u-r}) + 9.25 & \hbox{for } {u-r} > 2.175 \nonumber \\ {u-r} > 2.22, & \hbox{whether there is no}\\ &\hbox{\textit {GALEX} counterpart.} \end{array} \right. \end{eqnarray*}$$

As can be seen in Figure 9, this selection seems more accurate to differentiate star-forming galaxies from passive galaxies than the u − r color cut proposed by Strateva et al. (2001). The broken line traces the minimum in the density of data points of (NUV − r) versus (u – r) diagram between the maximum of density regarding the "red sequence" and the more extended maximum tracing the "blue cloud." The left side of the frontier tries to include in the passive galaxy side the locus of evolved "E+A" galaxies in a UV–optical diagram (Kaviraj et al. 2007). In the case where there is no UV data for a galaxy, we apply the Strateva's u – r cut.

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In a forthcoming paper (Hernández-Fernández et al. 2011a), we take advantage of this UV–optical color frontier in order to make up a sample of star-forming galaxies in clusters. We analyze the spatial variation of distributions of spectral properties for this sample of star-forming galaxies. We find statistically significant differences, applying a Kolmogorov–Smirnov test, in those distributions throughout different environments, i.e., virial regions, infall regions, and field environment.

Figures 10 and 11 show the sky distribution in ABELL 1185 of giant galaxies M_r  < −19.5 and low-luminosity galaxies −19.5 < M_r  < −18, respectively. Both figures also show the sky distribution of star-forming and passive galaxies.

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In Figure 10, it can be seen that the main concentration of giant galaxies from the virial region of ABELL 1185 around R.A. ∼ 16775, decl. ∼ 28.5 is framed by the dashed circle. In the same way, there are evident galaxy agglomerations around the virial region of ABELL 1185 with less structural entity than ABELL 1185, except for the group of galaxies in the south side around R.A. ∼ 1678, Decl. ∼ 27.5. We check the redshift distribution of galaxies around this location and find an evident dynamical structure around z = 0.034. This aggregate of galaxies, showing a strikingly high fraction of passive giant galaxies, can be linked with the "bare" massive-cluster cores identified by Poggianti et al. (2006). Poggianti et al. (2006) propose, as a hypothesis, that systems close to more massive structures, thus embedded in a massive superstructure, have a different galactic content than completely isolated galaxy systems of similar mass. They suggest that these objects lived in regions that were very dense at high redshift but failed to acquire star-forming galaxies at later times, possibly due to the characteristics of their surrounding supercluster environment. On the other hand, the maxima in the sky distribution of passive giant galaxies trace the central position of the main structures as ABELL 1185 and the "bare core" at the south side, while star-forming galaxies occupy these regions with a more spread distribution, following the general trend for clustering depending on spectral type found in astrophysical observations and simulations (e.g., Madgwick et al. 2003; Springel et al. 2005).

We plot the sky distribution of low-luminosity −19.5 < M_r  < −18 galaxies in Figure 11. These galaxies show a more continuous sky distribution around the central region of ABELL 1185 connecting this region with the structures in the south, east, and west side of the cluster. This is in good agreement with a less clustered low-luminosity population as suggested in the literature (e.g., Norberg et al. 2002; Springel et al. 2005). The star-forming galaxies occupy both the densest regions and less dense regions, but the passive galaxies seem to preferably inhabit the central region of the structures, avoiding the field environment in the same way as observed by Haines et al. (2006).

We expose the main results and conclusions of this paper in this itemized summary.

• 1.  
  We compile a sample of galaxies which inhabits in clusters showing a broad range of cluster properties (σ_c, morphology, etc). This galaxy sample is observed down to the luminosity frontier between giant and dwarf galaxies by the Main Galaxy Sample of SDSS and other galaxy surveys from UV to FIR. We build a spectrophotometric catalog for this cluster galaxy sample with a detailed photometry for each galaxy in order to be accurate for spectral template fitting.
• 2.  
  The clusters from the sample with X-ray detections or confident upper limits are consistent with the X-ray luminosity versus cluster velocity dispersion L_X∝σ^4.4_c trend found by Mahdavi & Geller (2001). The clusters with no X-ray fluxes in the literature can be reconciled with the L_X–σ_c trend assuming an upper limit in X-ray luminosity of 10⁴¹ erg s⁻¹, except for the case of WBL 205, a cluster with clear evidence of the presence of dynamical substructures.
• 3.  
  The galaxy density Σ₅ distribution of virial regions is biased to higher densities with respect to the Σ₅ distribution of the outskirts. The Σ₅ distribution of massive clusters (virial regions and the outskirts) shows similar ranges than the low-mass clusters, but they have higher averages of Σ₅ than the low-mass clusters and present a highest density tail which is missing in the low-mass clusters. The Σ₅ distribution of virial regions of massive clusters is statistically distinguishable, up to a ∼96% of probability, from the corresponding distribution for low-mass clusters. The overlapping of distributions of Σ₅ between virial regions and their outskirts at highest densities suggests the presence of galaxy structures in the outskirts as massive as the cluster cores.
• 4.  
  The Σ₅–r_P relation shows a more broad trend outside the virial region than the trend for the virial region, due to the presence of density peaks. Both the massive and low-mass clusters follow a similar trend inside the virial region, but the low-mass clusters avoid the highest density tail. This relation is well fitted by a King profile along the clustercentric radius, for both the massive and the low mass clusters.
• 5.  
  ABELL 1185 shows clear evidences of galaxy structures around the virial region. In this cluster, low-luminosity star-forming galaxies are distributed along more spread structures than their giant counterparts, whereas low-luminosity passive galaxies avoid the low-density environment. Giant passive and star-forming galaxies share rather similar sky regions with passive galaxies exhibiting more cuspy distributions.

J.D.H.F. thanks the Laboratoire d'Astrophysique de Marseille and L'Osservatorio Astronomico di Padova for hospitality during stays to carry out part of this work. Special thanks are given to Veronique Buat, Denis Burgarella, and Bianca M^a Poggianti for their help and advice during the first stages of this work.

J.D.H.F. acknowledges financial support from the Spanish Ministerio de Ciencia e Innovación under the FPI grant BES-2005-7570. We also acknowledge funding by the Spanish PNAYA project ESTALLIDOS (grants AYA2007-67965-C03-02, AYA2010-21887-C04-01) and project CSD2006 00070 "1st Science with GTC" from the CONSOLIDER 2010 program of the Spanish MICINN.

This publication has made use of the following resources.

• 1.  
  The NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
• 2.  
  The Sloan Digital Sky Survey (SDSS) database. Funding for the Sloan Digital Sky Survey (SDSS) and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, and the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web site is http://www.sdss.org/.The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, The University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, The Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.
• 3.  
  The Galaxy Evolution Explorer (GALEX), which is a NASA mission managed by the Jet Propulsion Laboratory and launched in 2003 April. We gratefully acknowledge NASA's support for the construction, operation, and science analysis for the GALEX mission, developed in cooperation with the Centre National d'Etudes Spatiales of France and the Korean Ministry of Science and Technology.
• 4.  
  The Two Micron All Sky Survey (2MASS), which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center at the California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.
• 5.  
  The NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.