The Galaxy Population of Low-Redshift Abell Clusters
aa r X i v : . [ a s t r o - ph . C O ] J u l The Galaxy Population of Low-Redshift Abell Clusters
Wayne A. Barkhouse, , H.K.C. Yee, , and Omar L´opez-Cruz , ABSTRACT
We present a study of the luminosity and color properties of galaxies selectedfrom a sample of 57 low-redshift Abell clusters. We utilize the non-parametricdwarf-to-giant ratio (DGR) and the blue galaxy fraction ( f b ) to investigate theclustercentric radial-dependent changes in the cluster galaxy population. Com-posite cluster samples are combined by scaling the counting radius by r tominimize radius selection bias. The separation of galaxies into a red and blue pop-ulation was achieved by selecting galaxies relative to the cluster color-magnituderelation. The DGR of the red and blue galaxies is found to be independent ofcluster richness ( B gc ), although the DGR is larger for the blue population at allmeasured radii. A decrease in the DGR for the red and red+blue galaxies isdetected in the cluster core region, while the blue galaxy DGR is nearly inde-pendent of radius. The f b is found not to correlate with B gc ; however, a steadydecline toward the inner-cluster region is observed for the giant galaxies. Thedwarf galaxy f b is approximately constant with clustercentric radius except forthe inner cluster core region where f b decreases. The clustercentric radial depen-dence of the DGR and the galaxy blue fraction, indicates that it is unlikely thata simple scenario based on either pure disruption or pure fading/reddening candescribe the evolution of infalling dwarf galaxies; both outcomes are producedby the cluster environment. Subject headings: galaxies: clusters: general — galaxies: luminosities: colors —galaxies: dwarf — galaxies: formation — galaxies: evolution Department of Physics and Astrophysics, University of North Dakota, Grand Forks, ND 58202; email:[email protected] Department of Astronomy and Astrophysics, University of Toronto, Toronto, ON, Canada, M5S 3H8;email: [email protected] Instituto Nacional de Astrof´ısica, Optica y Electr´onica, Tonantzintla, Pue., M´exico; email:[email protected] Visiting Astronomer, Kitt Peak National Observatory. KPNO is operated by AURA, Inc. under contractto the National Science Foundation.
1. Introduction
A fundamental goal in the study of galaxy clusters is to understand the role of environ-ment on galaxy formation and evolution. The well-established morphology–density relation(e.g., Dressler 1980; Dressler et al. 1997; Thomas & Katgert 2006) highlights the impact oflocation on the properties of cluster galaxies: the cores of rich clusters are inundated withearly-type galaxies while the outskirts contain a large fraction of late-type systems (for ex-ample, see Abraham et al. 1996; Morris et al. 1998; Treu et al. 2003; Smith et al. 2006).A theoretical understanding of the morphology–density relation has focused on the influ-ence of dynamical factors on the formation of early-type galaxies in high-density regions viathe merger of late-type galaxies (e.g., Okamoto & Nagashima 2001). Mergers are expectedto occur with greater ease in areas where the galaxy velocity dispersion is low (e.g., Merritt1984). This is in contrast to the high-velocity dispersion regions found at the center of richclusters (e.g., Rood et al. 1972; Kent & Gunn 1982; Dubinski 1998). To solve this apparentcontradiction with the observed morphology–density relation, it is hypothesized that thetransformation of late- into early-type galaxies occurred in group-like environments with alow-velocity dispersion, as clusters assembled from the gravitational infall of matter (e.g.,Roos & Norman 1979; McIntosh et al. 2004). Once a cluster has formed, other dynamicalprocesses, such as galaxy harassment and ram pressure stripping, will determine the cur-rent morphological makeup of the cluster galaxy population (Moore et al. 1996; Abadi et al.1999; Quilis et al. 2000; Boselli & Gavazzi 2006).In general, low-redshift clusters contain two major galaxy populations; a red, evolved,early-type component that dominates the central cluster region, and a blue, late-type pop-ulation which has undergone relatively recent star formation and is most prominent in theoutskirts of clusters (for example, see Abraham et al. 1996; Wake et al. 2005; Wolf et al.2007). This has led to the hypothesis that clusters are built-up gradually from the infall offield galaxies (e.g., Ellingson et al. 2001; Treu et al. 2003; McIntosh et al. 2004).Understanding how different sub-populations of galaxies evolve in clusters can be furtherelucidated by subdividing the cluster galaxy population with respect to luminosity and color.Barkhouse et al. (2007, hereafter B07) demonstrated that, in general, the faint-end slope ofthe cluster luminosity function (LF) becomes steeper with increasing clustercentric distance.In this paper we examine the radial dependence of galaxy luminosity by measuring thedwarf-to-giant ratio (DGR) as a function of clustercentric radius. Unlike the galaxy LF, thenon-parametric DGR provides a robust measure of the relative fraction of faint-to-brightgalaxies without assuming a specific functional form for the LF. In L´opez-Cruz et al. (2004)we presented evidence for a blueward shift in the color-magnitude relation (CMR) withincreasing clustercentric radius, utilizing the same cluster sample as this paper. To quantify 3 –changes in galaxy color as a function of clustercentric radius, we search for radius-dependentchanges in the blue galaxy fraction ( f b ).This paper is the third in a series resulting from a large multi-color imaging surveyof low-redshift Abell galaxy clusters. This paper is organized as follows. In § § §
4. Wecompare our findings with published results in § §
6. Finally wesummarize our conclusions in § z < . H = 50 h km s − Mpc − and q = 0, unless otherwise indicated.
2. Observations and Data Reductions
We present in this section a brief summary of the cluster sample selection criteria, obser-vations, data reductions, and photometric measurements. We refer the reader to L´opez-Cruz(1997), Barkhouse (2003), L´opez-Cruz et al. (2004), and B07 for further details.The Abell clusters in our sample are selected mainly from the catalog of
Einstein -detected bright X-ray clusters compiled by Jones & Forman (1999). This sample includes47 clusters observed at KPNO using the 0.9 m telescope plus the T2kA (2048 × . ′′ pixel − ) CCD detector (the LOCOS sample; L´opez-Cruz 1997; Yee & L´opez-Cruz 1999;L´opez-Cruz 2001, O. L´opez-Cruz et al. 2009, in preparation). These clusters were chosento be at high galactic latitude ( | b | ≥ ◦ ) and within the redshift range of 0 . ≤ z ≤ . . ≤ z ≤ . × . ′′ pixel − ) from Barkhouse (2003)were incorporated in our sample. In addition, two clusters from Brown (1997) using thesame instrumental setup as the LOCOS sample and selection criteria as the mosaic data areincluded.The integration times for our 57-cluster sample varies from 250 to 9900 s, depending 4 –on the filter ( B or R C ) and the redshift of the cluster. For this study we use a total of fivecontrol fields, which were chosen at random positions on the sky at least 5 ◦ away from theclusters in the sample. These control fields were observed using the MOSAIC camera to acomparable depth and reduced in the same manner as the cluster data.Preprocessing of the images was done using IRAF and photometric reductions werecarried out using the program PPP (Picture Processing Package; Yee 1991), which includesalgorithms for performing automatic object finding, star/galaxy classification, and totalmagnitude determination. Galaxy colors are measured using fixed apertures on the images ofeach filter, sampling identical regions of galaxies in different filters. Instrumental magnitudesare calibrated to the Kron-Cousins system by observing standard stars from Landolt (1992).The 100% completeness limit of each field was set at 1.0 mag brighter than the magnitudeof a stellar object with a brightness equivalent to having a S/N=5 in an aperture of 2 ′′ (see Yee 1991). Extinction values for each cluster are taken from the maps and tables ofBurstein & Heiles (1982) and Burstein & Heiles (1984).
3. The Dwarf-to-Giant Ratio
In B07 we presented the composite cluster LF for the sample of 57 low-redshift clus-ters utilized in this paper. We found that, in general, the slope of the faint-end of the LFrises with increasing clustercentric radius. To further expand upon these findings, we havedivided the cluster galaxy population into two sub-samples based on luminosity. Follow-ing the nomenclature used by previous studies (e.g., L´opez-Cruz 1997; Driver et al. 1998;Popesso et al. 2006), we classify galaxies brighter than M R c = −
20 as “Giants” and thosehaving − . ≤ M R c ≤ − . M ∗ R c calculated from these data(see B07) is M ∗ R c ∼ − .
3. The measurement of R c , including applied k-corrections, isdescribed in B07. These magnitude cuts allow us to maximize the number of galaxies toimprove statistical inferences while ensuring that incompleteness effects at the faint-end arenegligible. We construct the cluster DGR by dividing the number of background-correcteddwarfs by the number of background-corrected giants. Only clusters 100% photometricallycomplete to M R c = −
17 are included in the construction of the DGR. Using this definition,we can employ the DGR to explore the change in the relative fraction of dwarfs and giantswith respect to various cluster characteristics.The primary advantage of using the DGR is that it provides a non-parametric measure-ment of the relative change in the number of giant and dwarf galaxies that is independent ofthe functional form selected for the cluster LF (e.g., a Schechter function; Schechter 1976). 5 –For example, a steepening of the faint-end slope of the LF with increasing clustercentricradius would be characterized by an increase in the DGR (Driver et al. 1998). Given thedegree of degeneracy between the parameters in a Schechter function fit (e.g., M ∗ and α ;B07), the DGR yields additional insights on the luminosity distribution of cluster galaxiesand thus complements the LF. To examine for a possible correlation of the DGR with cluster richness, we plot inFigure 1 the DGR versus the richness parameter B gc for 39 clusters that are 100% photo-metrically complete to M R c = − . B gc ,which is a measure of the cluster center–galaxy correlation amplitude (Yee & L´opez-Cruz1999; Yee & Ellingson 2003; B07). In Figure 1 galaxies are selected within ( r/r ) = 0 . ∼ h − kpc for the sample average) of each cluster center for the red+blue, red, and bluecluster galaxy populations. The red+blue cluster galaxy sample is compiled by includinggalaxies that have been culled of systems redder than the CMR for each individual cluster(for further details, see B07). The center of each cluster is chosen as the position of thebrightest cluster galaxy (BCG) or, when some doubt exists, the nearest bright early-typegalaxy to the X-ray centroid. The value of r , an approximate measure of the virial radius(Cole & Lacey 1996), is derived from the relationship between r and B gc as describedin B07 (see their equation 7), and is used as a scaling factor to minimize radial samplingbias due to variations in cluster richness (Christlein & Zabludoff 2003; Hansen et al. 2005;Popesso et al. 2006). The r measurements for our cluster sample are tabulated in Table1 of B07 ( r uncertainties are based on the 15% rms scatter in the derived value of r asexplained in B07). The radial sampling criterion of ( r/r ) = 0 . M R c = −
17, andthus provide a large number of net galaxy counts to minimize the statistical uncertainty inthe DGR.Analysis of Figure 1 for the red+blue galaxies (top panel) indicates that there is no signif-icant correlation between the DGR and cluster richness. A Kendell’s τ statistic (Press et al.1992) yields a 49% probability that the DGR and B gc are correlated. When not employinga dynamical counting radius (e.g. r ), the mean and dispersion of the DGR vs. B gc isincreased significantly, indicating that the DGR is dependent on clustercentric radius (see § ± .
22 mag (3 σ ) of the cluster CMR, while blue galaxies are selectedfrom the area of the color-magnitude diagram that is blueward of the region used to selectthe red galaxies (see Figures 5 and 8 from B07). As noted for the combined red+blue galaxypopulation, the red and blue galaxies have been background-corrected using the exact samecolor-selection criteria as for the cluster galaxies.Similar to the red+blue sample, we find no significant correlation between the DGRand richness when considering the red and blue galaxies separately. A Kendell’s τ statisticindicates a 67% probability that the red DGR is correlated with B gc ; while for the blue DGR,there is only a 3% probability that these quantities are correlated.For the red+blue population we find a mean DGR of 2 . +1 . − . , where the uncertaintiesbracket the interval containing 68% of the data points about the mean. For the red and bluesamples we have 1 . +0 . − . and 11 . +11 . − . , respectively. The dominance of the dwarfs forthe blue galaxy population compared to those in the red galaxy sample, is consistent withthe findings from B07 in the sense that the blue LFs were found to have a steeper faint-endslope than the red sequence LFs. It is interesting to note that the blue DGR has a largerrange of values and dispersion than the red DGR. This is an indication that blue galaxies inclusters have a larger variance in their properties and states of evolution compared to the redgalaxies, which can be seen as primarily dominated by the end products of galaxy evolutionand infall process. In B07 we presented evidence for a steepening of the LF faint-end slope towards thecluster outskirts. To investigate this correlation further, we plot in Figure 2 the DGR for thered+blue, red, and blue galaxy populations as a function of clustercentric radius. The DGRis plotted at the mid-point for the following annuli; ( r/r ) ≤ .
2, 0 . ≤ ( r/r ) ≤ . . ≤ ( r/r ) ≤ .
6, and 0 . ≤ ( r/r ) ≤ .
0. The DGR is constructed by stacking clustergalaxies appropriate for each radial bin, and the uncertainty is derived from the standarddeviation assuming Poisson statistics. Due to the variation in spatial imaging coverage, thenumber of clusters contributing to each radial bin is not equal. For all radius-dependentanalysis in this study, we only include clusters that have complete spatial coverage for theindicated clustercentric radius.Examination of Figure 2 shows that the DGR increases with radius for both the red and 7 –red+blue galaxy populations. For the red+blue galaxy sample the DGR is 2.7 times largerfor the outer-most annulus as compared to the inner-most radial bin (8 . σ difference). Forthe red galaxies the DGR is 2 . . σ level). The DGR for the blue galaxies is approximatelyconstant with perhaps even a possible decreasing trend with radius. We note that theuncertainties for the blue DGR are ∼ . σ level. Although the error bars are relatively large, thedistribution of the blue DGR suggests a mild rising trend with decreasing radius. The risein the DGR with increasing radius for the red+blue galaxy sample is a reflection of theincreasing dominance of blue galaxies with radius. This result is in agreement with Figures4 and 7 from B07 where the red and red+blue galaxy populations were found to have a risingfaint-end slope with increasing clustercentric radius. The faint-end slope of the blue galaxyLF depicted in Figure 10 from B07 indicates a much weaker dependence on radius.Figure 2 also shows that for all radii depicted, the DGR is larger for the blue than forthe red galaxy sample. This finding is also supported by the comparison of red and blue LFspresented in Figure 11 of B07, and is a result of the larger contribution of luminous galaxiesto the red sample as compared to the blue population.
4. The Blue Galaxy Fraction
A comparison of the number of red and blue galaxies gives a rough indication of therelative mixture of early- and late-type systems. Due to the poor seeing of our imaging data(fwhm ∼ . ′′ ), morphological classification of galaxies with magnitudes near the complete-ness limit are not reliable. We therefore elect to use a broad-band color selection technique,such as the fraction of blue galaxies ( f b ; the number of blue galaxies divided by the numberof red+blue galaxies), to glean some information about the galaxy population makeup ofour sample. We note that f b is constructed from background-corrected galaxy counts, andonly includes clusters that are 100% photometrically complete for the indicated magnituderange.” 8 – f b In Figure 3 we present f b versus cluster richness for galaxies selected within ( r/r ) =0 . f b with cluster richness, we plot f b for galaxies with M R c ≤ −
17 (14 clusters; opensymbols) and M R c ≤ −
19 (16 clusters; filled symbols). We note that the magnitude limitutilized by Butcher & Oemler (1984) to characterize the cluster blue fraction correspondsto M R c ∼ − . z ≤ . z ≤ . M R c ≤ − f b = 0 . ± .
08, while for the deep sample ( M R c ≤ −
17) we measure f b = 0 . ± . f b and B gc for either the deep or bright sample.A Kendell’s τ statistic yields a 59%(47%) probability of a correlation for the deep(bright)sample. Using ( r/r ) = 1 as our counting aperture also yields no significant correlationbetween f b and B gc . Using the equivalent Butcher & Oemler magnitude counting limit of M R c ∼ − .
5, we find f b = 0 . ± .
08. A Kendell’s τ statistic gives a 75% probability ofa correlation between f b and B gc . Thus no significant correlation between f b and clusterrichness is discernible when counting galaxies within an equivalent dynamical radius. Thisindicates that the galaxy population in clusters is not dependent on cluster richness if adynamics-dependent radius is used in sampling. f b To search for a possible radial dependence of f b , we plot in Figure 4 the f b in concentricannuli versus clustercentric radius for the dwarfs (open squares), giants (open triangles), andgiants+dwarfs (filled circles) samples. Several aspects of the galaxy f b are apparent: a) atany radius the dwarf galaxies have a greater f b than the giants, b) the giant f b increasesapproximately monotonically with increasing radius, with a five-fold increase from the innerto the outer radial bin (10 σ difference), and c) the dwarf f b , while decreasing by a factor of ∼ . r/r ) ∼ .
2. To determine the relative change in the red and blue galaxies for the two inner-mostradial bins, we restrict our cluster sample to include only those clusters that contribute toboth annuli. Using this common cluster sample we find that there is a 53% decrease in thenet number of blue dwarfs when comparing the 0 . ≤ ( r/r ) ≤ . r/r ) ≤ . increase for the red galaxies.To ascertain the relative change in the radial-dependence of f b as a function of mag-nitude, we present in Figure 5 the f b as a function of M R c for the four radial bins used inFigure 4 ( f b is constructed by counting galaxies in bins of one magnitude in width). Thisfigure demonstrates that f b for the outer-most annulus (0 . ≤ ( r/r ) ≤ .
0) is greater ateach magnitude interval than for f b measured for the inner annuli. In addition, f b for allfour radial bins generally show an increase when counting galaxies from progressively faintermagnitude bins. The data depicted in Figure 5 suggests that luminous galaxies may undergoa more rapid change in their color composition than the dwarf galaxies. For example, f b forgalaxies in the M R c = − . f b decreases by a factor of 1.5 for galaxies in the M R c = − . ∼ . σ difference in both cases).As a caveat we note that our results based on Figures 4 and 5 demonstrate that thecounting aperture and magnitude range has a direct impact on the value of f b , and thus onemust be cautious when comparing blue fractions for clusters with disparate masses from avariety of sources (see also, Ellingson et al. 2001; Fairley et al. 2002; De Propris et al. 2004;Popesso et al. 2007). f b To test for a correlation between f b and redshift, we present in Figure 6 the f b versusredshift for; a) galaxies brighter than M R c = −
19 (filled symbols; 54 clusters), b) galaxiesselected with M R c ≤ −
17 (open symbols; 39 clusters), and c) galaxies brighter than theequivalent Butcher & Oemler (1984) magnitude limit ( M R c = − .
5; solid triangles, 54clusters). The f b ’s depicted in Figure 6 are constructed by including only galaxies within aclustercentric radius of ( r/r ) = 0 .
4. A Kendell’s τ statistic for the M R c ≤ −
19 sampleyields a probability of 100% that f b and redshift are correlated. Inspection of Figure 6 showsthat f b increases with redshift for z & .
1. Restricting our analysis to clusters with z < . f b andredshift is due to clusters with z & .
1. For the cluster sample with M R c ≤ −
17, we findthat f b and redshift are correlated at the 90% significance level. Limiting the M R c ≤ − z ≤ . M R c = − . z < . f b and redshift are correlated at the 97% significance level. 10 –The correlation between f b and redshift is most-likely a reflection of the Butcher-Oemlereffect (Butcher & Oemler 1978, 1984), in which the fraction of blue cluster galaxies increaseswith look-back time. Since our cluster sample only extends to z ∼ .
2, we are not ableto make any firm conclusions on the redshift evolution of f b . However, Figure 6 and ourcorrelation measurements indicate that the Butcher-Oemler effect is magnitude-dependent,such that a fainter magnitude limit yields a larger effect. Unfortunately our M R c ≤ − z > .
5. Comparison with Other Results5.1. Dynamical versus Fixed Clustercentric Radius
A major goal of this paper is to examine the luminosity and color distribution of individ-ual and composite cluster galaxy populations. Many potential correlations may be obscuredwhen only a limited number of cluster galaxies are available. To mitigate this effect, we com-piled composite samples by stacking together galaxies from individual clusters. To minimizeradial sampling bias, we scaled each cluster’s counting aperture by r prior to combininggalaxy counts. Radial sampling bias can be problematic when comparing individual clusters(see, for example, B07), hence the need to scale clusters by a common dynamical radius.This point is aptly illustrated by the recent studies of Popesso et al. (2005, 2006) in whichsignificant correlations between the DGR and various cluster characteristics (mass, velocitydispersion, X-ray luminosity, and optical luminosity) were found when measuring the DGRusing a fixed metric aperture, but were much less significant when scaling the counting aper-ture by r . In a study by Margoniner et al. (2001) and Goto et al. (2003), cluster richnessand f b were found to be correlated such that poor systems have a higher f b , which we suggestis the result of using a fixed counting aperture. Our results, along with those from many others, show that quantities such as the DGRand f b are dependent on the luminosity definitions of the galaxy samples used. When com-paring different studies, care should be taken to account for any possible effects arisingfrom the galaxy luminosity or mass limits. Some recent studies have examined the DGRand f b based on spectroscopic data from the SDSS galaxy sample (e.g., Aguerri et al. 2007;Popesso et al. 2007; S´anchez-Janssen et al. 2008). However, these studies, while statisti-cally more robust, are in general much shallower than investigations applying a statistical 11 –background correction method to photometric data. Studies using photometric galaxy sam-ples, going typically two to three magnitudes deeper, provide considerably larger leverage insampling the dependence of galaxy population and evolution on luminosity/mass. In a study by De Propris et al. (2003) the DGR for a spectroscopically-measured sampleof 60 clusters from the 2dF Galaxy Redshift Survey were presented. Transforming theirmagnitudes to R C (Fukugita et al. 1995) and adopting our cosmology, De Propris et al.defines giants as those galaxies with − . ≤ M R c ≤ − . − . ≤ M R c ≤− .
9. They divide their galaxies based on spectroscopic type, and find DGR = 1 . ± . . ± .
97 for galaxies with early- and late-type spectra, corresponding to our red andblue galaxy samples. Their results show the same trend as ours for red and blue galaxies( § h − Mpcin radius rather than scaling relative to a dynamical radius. They also determined that theDGR is smaller for galaxies selected within 0 . h − Mpc of the composite cluster center ascompared to galaxies at larger radii (2 . σ difference). In Figure 2 we showed that the DGRincreases with clustercentric radius for the red+blue cluster galaxy population (at the 8 σ level). We suggest that the De Propris et al. result would be of higher significance if thecluster counting aperture was scaled by a common dynamical radius.In addition, De Propris et al. reports no statistically significant correlation ( . σ )between cluster velocity dispersion (divided at σ = 800 km s − ) and the DGR, or between“rich” and “poor” clusters. This result is consistent with our data depicted in Figure 1, eventhough we sample 2 mag deeper than De Propris et al. and scale by r .From a study of 69 clusters selected from the RASS-SDSS catalog, Popesso et al. (2006)presented the DGR for various cluster galaxy sub-samples. Using r as a scaling fac-tor, Popesso et al. found that the DGR is not correlated with cluster mass (i.e., M ),velocity dispersion, or L X . This result is compatible with our findings depicted in Fig-ure 1, where we find no significant correlation between the DGR and B gc for the red+blue,red, and blue galaxy populations. Using the L X (0.1-2.4 keV) measurements compiled byEbeling et al. (1996, 2000), we examined the DGR vs. L X distribution for galaxies selectedwithin ( r/r ) ≤ .
4. Applying the Kendell’s τ statistic to the red+blue/red/blue galaxy 12 –samples, we find a 87%/91%/29% probability that the DGR and L X are correlated. Ourfindings support the results of Popesso et al. in that there is no strong, statistically significantcorrelation between the DGR and L X . In addition to examining the DGR for the composite cluster galaxy population, Popessoet al. divided their sample into red and blue galaxies by adopting u − r = 2 .
22 as thedividing color threshold. Popesso et al. found that the fraction of red and blue dwarfgalaxies decreases toward the cluster center (see their Figure 12a). Although they plot thecumulative fractional change in the number of dwarfs, it is apparent that Popesso et al.detects a more statistically significant drop in the fraction of blue dwarf galaxies than whatwe find (see Figure 2). This difference may be related to their utilization of the u − band forthe selection of blue galaxies and differences in the magnitude range used to define giantsand dwarfs.In Figure 12b from Popesso et al., the dwarf red-to-blue ratio (RBR) is depicted asa function of clustercentric radius, normalized to r . This figure shows that the RBR isapproximately constant from 0 . < ( r/r ) < . ∼ .
6) and increases to ∼ . f b is consistent with thisresult. Computing RBR for our dwarf sample yields RBR ∼ . r/r ) > . ∼ . z < .
1) selected from the SDSS-DR4 data set. The blue galaxy fraction wasconstructed by including galaxies brighter than M r = −
20 and located within a radius of( r/r ) = 1. Aguerri et al. found that f b is correlated with L X in the sense that low f b clusters have a greater L X (3 σ difference). This result is also supported by Popesso et al.(2007), who measured f b for a sample of 79 clusters from the RASS-SDSS cluster catalog byincluding spectroscopically-detected galaxies within ( r/r ) = 1.Using X-ray data from Ebeling et al., we find that f b and L X are not significantlycorrelated. For galaxies brighter than M R c = −
17 and located within ( r/r ) = 0 .
4, aKendall’s τ statistic yields a 17% probability of a correlation. Restricting our analysis to M R c ≤ −
19, we find a 65% probability of a correlation. Transforming the magnitude limitutilized by Aguerri et al. to our filter and distance scale ( M R c ∼ − f b and L X .Utilizing a sample of 60 clusters ( z < .
11) selected spectroscopically from the 2dFGRS,De Propris et al. (2004) searched for correlations between f b and various cluster properties.In Figure 5 we showed that f b is sensitive both to the adopted absolute magnitude rangeand clustercentric distance used to select galaxies. De Propris et al. reached a similarconclusion by determining that f b increases both with decreasing luminosity and increasingclustercentric radius. In addition, De Propris et al. also found that there is no significantcorrelation between f b and cluster richness when measured within ( r/r ) = 0 .
5. This isalso in agreement with our results depicted in Figure 3.
6. Discussion
In this study we have examined the radial dependence of the luminosity and colordistribution of cluster galaxies by utilizing the DGR and f b . Scaling the galaxy countingaperture relative to r , allows us to minimize radial sampling bias.The main results highlighted in this paper and encapsulated in Figures 2 and 4, suggeststhat some type of dynamical mechanism may be responsible for the decline in the numberof blue dwarf galaxies relative to the corresponding red systems in the cluster core region.The trend depicted in Figure 2 implies that the DGR for the blue galaxies is approximatelyconstant with radius. This suggests that the decrease in the number of blue dwarfs towardthe cluster center is accompanied by a decline in the number of blue giants, thus maintaininga roughly constant DGR. A decrease in f b for the giant galaxies in the cluster core region(Figure 4) and a drop in the DGR for the red galaxies, suggests that the relative fraction ofred giants increases toward the cluster center.These results support the general view that blue galaxies dominate the galaxy populationin the outskirts of clusters in contrast to the central cluster region (e.g., Ellingson et al.2001; Fairley et al. 2002; Dahl´en et al. 2004; Tran et al. 2005). They also imply that fieldgalaxies, which are generally bluer than cluster galaxies (see, for example, Lewis et al. 2002;McIntosh et al. 2004), fall into the cluster environment, turn red (possibly via some processthat truncates star formation), and that blue dwarf galaxies get preferentially disrupted ortransformed into red dwarfs at small clustercentric radii. 14 – These findings are open to several possible interpretations: a) blue and red dwarfs getdisrupted tidally or undergo mergers with giant galaxies at roughly the same rate, whichdestroy individual dwarfs, except in the cluster core region, where the red dwarfs have ahigher survival rate; b) the average dwarf galaxy star formation rate remains relatively un-changed until the dwarfs reach the central cluster region, where the influence of ram pressureand cluster tidal effects are expected to be maximized (e.g., Moore et al. 1996), quenchingstar formation and transforming the galaxies into red dwarfs; and c) the transformation rateof blue into red galaxies resulting from the quenching of star formation is more efficient ingiants than in dwarfs (see Figure 5).The first interpretation requires that blue dwarfs are more susceptible than red dwarfsto destructive forces in the cluster central region. Some support for this idea is garnered fromthe fact that blue dwarfs are very similar to the low-mass dwarf spheroidal galaxies, which areexpected to undergo tidal disruption in the cluster environment (Thompson & Gregory 1993;Gallagher & Wyse 1994; Moore et al. 1999; Quilis et al. 2000; Boyce et al. 2001; Barai et al.2007). These galaxies may potentially be the source of tidally-disrupted material that helpedto form the halo of cD galaxies (L´opez-Cruz et al. 1997b; Hilker et al. 1999, 2003). The reddwarf galaxies, however, may be part of a population of nucleated dwarfs (van den Bergh1986; Caldwell & Bothun 1987; Lisker et al. 2007) that would be expected to have a deepergravitational potential well than the more diffuse dwarf spheroidal population. This wouldallow them to more efficiently survive cluster tidal forces against disruption than the dwarfspheroidals (i.e., non-nucleated dwarf galaxies; see, for example, Thompson & Gregory 1993;Trujillo et al. 2002; Barai et al. 2007; Lisker et al. 2007). Nucleated cluster dwarf galaxieshave been shown to have colors that are redder on average than non-nucleated dwarfs (e.g.Caldwell & Bothun 1987; Lisker et al. 2007), and thus supports the suggestion that the reddwarf population is composed mainly of nucleated dwarf galaxies.For the second scenario, if the blue dwarf galaxies in the cluster core have been strippedof their gas, had their star formation truncated, and transformed into red dwarf galaxies,we would expect that the number of red dwarf galaxies would increase with decreasingclustercentric radius. Figure 2 indicates that the red DGR actually decreases by a factorof ∼ f b from the outer- tothe inner-cluster region than the less luminous galaxy population. However, this seems anunlikely explanation, since, with their lower gravitational potential, we would expect mostphysical mechanisms in clusters that affects star formation rate in galaxies should have arelatively larger effect in dwarf galaxies than the more massive giant galaxies. The tidal disruption of dwarf galaxies is a possible physical mechanism to help explainthe observations presented in this paper; an alternative interpretation is that the blue dwarfgalaxies simply fade and turn red as they fall toward the cluster center, and are subsequentlydetected as red galaxies in the inner-cluster region. This idea is not unreasonable if we expectthat star formation for infalling dwarf galaxies gets truncated, with an ensuing passive evo-lution of the stellar population (e.g., Abraham et al. 1996; Ellingson et al. 2001; Treu et al.2003; Smith et al. 2006).The exact number of blue dwarfs that would be expected to fade and turn red, or to dis-appear due to disruption or merger, cannot be accurately calculated with a simple toy model.A detailed N-body simulation that incorporates a complete accounting of stellar evolutionand traces the evolutionary path of each dwarf galaxy would be required. Nevertheless, weare able to place limits on the fraction of blue dwarfs that have faded and turned red orthat have been disrupted, by comparing the blue-to-red dwarf luminosity ratio between theinner- ( L ib /L ir ) and outer-most ( L ob /L or ) radial bins.For the case of pure disruption, L ob /L or was measured for the 0 . ≤ ( r/r ) ≤ . − . ≤ M R c ≤ − . L ob /L or = 1 . r/r ) ≤ . L ib /L ir was calculated in the same manner asfor the outer radial bin except that the magnitude distribution of the background-correctedgalaxies is determined using the deprojected cluster LF (see B07). The deprojected LF isconstructed by subtracting the contribution of galaxies located in the cluster outskirts thatare projected onto the central cluster region. The deprojected LF thus provides a moreaccurate estimate of the galaxy luminosity distribution in the cluster center, especially at 16 –the faint end. Using the deprojected LF, we found that L ib /L ir = 0 .
34. Using our measuredluminosity ratios, the expected fraction of disrupted blue dwarfs ( f ) can be estimated from L ib /L ir = (1 − f )( L ob /L or ). Solving for f we find f = 0 .
81, and thus approximately 81% of theblue dwarfs would undergo disruption as they fall into the central cluster region.For the case of pure fading with an associated reddening, we employ the library of evolu-tionary stellar population synthesis models computed using the isochrone code of Bruzual & Charlot(2003), to provide an estimate of the amount of fading/reddening that an infalling galaxywould experience. Adopting the concordance cosmological parameters (i.e., Ω m = 0 . λ = 0 .
7, and H = 70 h km s − Mpc − ), the timescale for the infall of a typical dwarfgalaxy from the 0 . ≤ ( r/r ) ≤ . r and the mean velocity dispersion for our 57-cluster sample. Using the B gc values tabu-lated in B07, and the relationship between B gc and velocity dispersion from Yee & Ellingson(2003), we find r = 1 . h − Mpc and σ v = 840 km s − . Using these values yields an averageinfall timescale of approximately 2 Gyr. Adopting a Salpeter Initial Mass Function (Salpeter1955), solar metallicity, a single-burst star formation model to simulate star formation trun-cation, and a 2 Gyr time-frame for passive evolution, we predict a ∼ . R C and a reddening of ∆( B − R c ) ∼ . . ≤ ( r/r ) ≤ . − . ≤ M R C ≤ − . L ob /L or = 1 .
46. The expected fraction of galaxies that have undergone fading with anassociated reddening can be estimated from L ib /L ir = (1 − f ) L ob / ( L or + f L ob ). Solving for f yields f = 0 .
57, and thus approximately 57% of the blue dwarfs would be expected to haveundergone fading and reddening as they reach the inner-cluster region.It seems unlikely, however, that a pure fading scenario can explain the change of f b seenfrom the outer to inner region of clusters. First, applying the same procedure of fading tothe giant galaxies, we find that 80% of the blue galaxies are expected to undergo fading andreddening by the time they reach the cluster core from the outer annulus. This is likelythe dominant cause for the change in f b for the giant galaxies, since it is unlikely that theycan be easily destroyed by tidal forces. If we assume that blue dwarfs were to fade by thesame fraction, then the dwarf blue-to-red luminosity would be much smaller than what ismeasured, by a factor of about ∼ .
5; unless some mechanism is invoked that fades bluegiant galaxies by a much larger fraction than dwarf galaxies. Due to the lower gravitationalpotential possessed by dwarf galaxies, almost all mechanisms used to explain the hasteningof galaxy evolution in rich environments operate equally or more efficiently for lower-mass 17 –galaxies; e.g., ram pressure, tidal interactions, harassment, etc. The only mechanism thatcould produce a higher fraction of the quenching of star formation in massive galaxies isAGN feedback, in that more massive galaxies will be more likely to contain a massive blackhole required for the AGN activity.Second, in a pure fading scenario for dwarf galaxies, we would expect the DGR for theblue+red sample to stay approximately constant from the outer to the inner region, unlessthe parent populations of galaxies from which the inner and outer regions are drawn fromare very different. Instead, we find there is a factor of 4 difference in the DGR between theouter annulus and the cluster core.While the discussion above cannot completely rule out the pure fading scenario, it seemslikely that, as blue dwarf galaxies fall into the cluster core, at least some fraction of themwill be destroyed either by tidal disruption, or mergers with larger galaxies.
7. Conclusions
In this paper we have studied the luminosity and color properties of a sample of 57low-redshift Abell clusters. Our main conclusions are:1) The DGR for the red, blue, and red+blue cluster galaxies are independent of clusterrichness when scaling the counting aperture by a dynamical radius (i.e., r ). Also, theDGR for blue galaxies is larger than for red systems.2) The DGR for the red galaxies decreases in the inner cluster region, while the blueDGR is approximately constant as a function of cluster-centric radius.3) The f b was found not to correlate with cluster richness when counting galaxies withina dynamical radius; however, it is found to be correlated with the adopted counting apertureand magnitude limit.4) The f b for dwarf galaxies was found to be approximately constant with clustercentricradius except in the cluster core region where f b decreases.5) The f b for giant galaxies was found to increase with clustercentric radius for allmeasured annuli.6) Based on the clustercentric radial dependence of the DGR and the galaxy blue frac-tion, it is unlikely that either a pure disruption or a pure fading/reddening scenario candescribe the evolution of infalling dwarf galaxies; both outcomes are produced by the clusterenvironment. 18 –We thank the anonymous referee for reviewing our paper. Research by W. A. B. issupported by a start-up grant from the University of North Dakota. Research by H. K. C.Y. is supported by an NSERC Discovery grant. O. L.-C research is supported by INAOEand a CONACyT grant for Ciencia B´asica P45952-F. O. L.-C. acknowledges support froma research grant from the Academia Mexicana de Ciencias-Royal Society during 2006-2007taken to the University of Bristol. OLC acknowledges Gus Oemler for suggesting the use ofnon-parametric DGR. We thank Huan Lin for providing photometric catalogs for five controlfields, and James Brown for the use of his galaxy profile fitting software and photometricdata for A496 and A1142.The Image Reduction and Analysis Facility (IRAF) is distributed by the National Op-tical Astronomy Observatory, which is operated by AURA, Inc., under contract to the Na-tional Science Foundation. This research has made use of the NASA/IPAC ExtragalacticDatabase (NED) which is operated by the Jet Propulsion Laboratory, California Instituteof Technology, under contract with the National Aeronautics and Space Administration. 19 – REFERENCES
Abadi, M. G., Moore, B., & Bower, R. G. 1999, MNRAS, 308, 947Abraham, R. G., et al. 1996, ApJ, 461, 694Aguerri, J. A. L., S´anchez-Janssen, R., & Mu˜noz-Tu˜n´on, C. 2007, A&A, 471, 17Barai, P., Brito, W., & Martel, H. 2007, preprint (astro-ph/0707.1533)Barkhouse, W. A. 2003, Ph.D. Thesis, Univ. TorontoBarkhouse, W. A., Yee, H. K. C., & L´opez-Cruz, O. 2007, ApJ, 671, 1471Boselli, A., & Gavazzi, G. 2006, PASP, 118, 517Boyce, P. J., Phillipps, S., Jones, J. B., Driver, S. P., Smith, R. M., & Couch, W. J. 2001,MNRAS, 328, 277Brown, J. P. 1997, Ph.D. thesis, Univ. TorontoBruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000Burstein, D., & Heiles, C. 1982, AJ, 87, 1165Burstein, D., & Heiles, C. 1984, ApJS, 54, 33Butcher, H., & Oemler, A. 1978, ApJ, 219, 18Butcher, H., & Oemler, A. 1984, ApJ, 285, 426Caldwell, N., & Bothun, G. D. 1987, AJ, 94, 1126Christlein, D., & Zabludoff, A. I. 2003, ApJ, 591, 764Cole, S., & Lacey, C. 1996, MNRAS, 281, 716Dahl´en, T., Fransson, C., ¨Ostlin, G., & N¨aslund, M. 2004, MNRAS, 350, 253De Propris, R., et al. 2003, MNRAS, 342, 725De Propris, R., et al. 2004, MNRAS, 351, 125Dressler, A. 1980, ApJ, 236, 351Dressler, A., et al. 1997, ApJ, 490, 577 20 –Driver, S. P., Couch, W. J., & Phillipps, S. 1998, MNRAS, 301, 369Dubinski, J. 1998, ApJ, 502, 141Ebeling, H., Voges, W., B¨ohringer, H., Edges, A. C., Huchra, J. P., & Briel, U. G. 1996,MNRAS, 281, 799Ebeling, H., Edges, A. C., Allen, S. W., Crawford, C. S., Fabian, A. C., & Huchra, J. P.2000, MNRAS, 318, 333Ellingson, E., Lin, H., Yee, H. K. C., & Carlberg, R. G. 2001, ApJ, 547, 609Fairley, B. W., Jones, L. R., Wake, D. A., Collins, C. A., Burke, D. J., Nichol, R. C., &Romer, A. K. 2002, MNRAS, 330, 755Fukugita, M., Shimasaku, K., & Ichikawa, T. 1995, PASP, 107, 945Gallagher, J. S., & Wyse, R. F. G. 1994, PASP, 106, 1225Goto, T., et al. 2003, PASJ, 55, 739Hansen, S. M., McKay, T. A., Wechsler, R. H., Annis, J., Sheldon, E. S., & Kimball, A.2005, ApJ, 633, 122Hilker, M., Infante, L., & Richtler, T. 1999, A&AS, 138, 55Hilker, M., Mieske, S., & Infante, L. 2003, A&A, 397, L9Jones, C., & Forman, W. 1999, ApJ, 511, 65Kent, S. M., & Gunn, J. E. 1982, AJ, 87, 945Landolt, A. U. 1992, AJ, 104, 372Lewis, I., et al. 2002, MNRAS, 334, 673Lisker, T., Grebel, E. K., Binggeli, B., & Glatt, K. 2007, ApJ, 660, 1186L´opez-Cruz O. 1997, Ph.D. Thesis, Univ. TorontoL´opez-Cruz, O., Yee, H. K. C., Brown, J. P., Jones. C., & Forman, W. 1997, ApJ, 475, L97L´opez-Cruz, O. 2001, Rev. Mex. Astron. Astrophys, Conf. Ser., 11, 183L´opez-Cruz, O., Barkhouse, W. A., & Yee, H. C. K. 2004, ApJ, 614, 679 21 –Margoniner, V. E., De Carvalho, R. R., Gal, R. R., & Djorgovski, S. G. 2001, ApJ, 548,L143McIntosh, D. H., Rix, H.-W. & Caldwell, N. 2004, ApJ, 610, 161Merritt, D. 1984, ApJ, 276, 26Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996, Nature, 379, 613Moore, B., Lake, G., Quinn, T., Stadel, J. 1999, MNRAS, 304, 465Morris, S. L., Hutchings, J. B., Carlberg, R. G., Yee, H. K. C., Ellingson, E., Balogh, M. L.,Abraham, R. G., & Smecker-Hane, T. A. 1998, ApJ, 507, 84Okamoto, T., & Nagashima, M. 2001, ApJ, 547, 109Popesso, P., B¨ohringer, H., Romaniello, M., & Voges, W. 2005, A&A, 433, 415Popesso, P., Biviano, A., B¨ohringer, H., & Romaniello, M. 2006, A&A, 445, 29Popesso, P., Biviano, A., Romaniello, M., & B¨ohringer, H. 2007, A&A, 461, 411Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical Recipes,The Art of Scientific Computing, (2d ed.; Cambridge: Cambridge University Press)Quilis, V., Moore, B., & Bower, R. 2000, Science, 288, 1617Rood, H. J., Page, T. L., Kintner, E. C., & King, I. R. 1972, ApJ, 175, 627Roos, N., & Norman, C. A. 1979, A&A, 76, 75Salpter, E. E. 1955, ApJ, 121, 161S´anchez-Janssen, R., Aguerri, J. A. L., & Mu˜noz-Tu˜n´on, C. 2008, ApJ, 679, L77Schechter, P. 1976, ApJ, 203, 297Smith, R. J., Hudson, M. J., Lucey, J. R., Nelan, J. E., & Wegner, G. A. 2006, MNRAS,369, 1419Thomas, T., & Katgert, P. 2006, A&A, 446, 31Thompson, L. A., & Gregory, S. A. 1993, AJ, 106, 2197Thuan, T. X., & Gunn, J. E. 1976, PASP, 88, 543 22 –Tran, K.-V. H., van Dokkum, P., Illingworth, G. D., Kelson, D., Gonzalez, A., & Franx, M.2005, ApJ, 619, 134Treu, T., Ellis, R. S., Kneib, J.-P., Dressler, A., Smail, I., Czoske, O., Oemler, A., &Natarajan, P. 2003, ApJ, 591, 53Trujillo, I., Aguerri, J. A. L., Guti´errez, C. M., Caon, N., & Cepa, J. 2002, ApJ, 573, 9van den Bergh, S. 1986, AJ, 91, 271Wake, D. A., Collins, C. A., Nichol, R. C., Jones, L. R., & Burke, D. J. 2005, ApJ, 627, 186Wolf, C., Gray, M. E., Arag´on-Salamanca, A., Lane, K. P., & Meisenheimer, K. 2007,MNRAS, 376, L1Yee, H. K. C. 1991, PASP, 103, 396Yee, H. K. C., & L´opez-Cruz, O. 1999, AJ, 117, 1985Yee, H. K. C. & Ellingson, E. 2003, ApJ, 585, 215
This preprint was prepared with the AAS L A TEX macros v5.2.
23 –
Red+Blue Dw a r f - t o - G i an t R a t i o Red B gc (Mpc ) Blue
Fig. 1.— Comparison of the DGR with cluster richness for the red+blue ( top ), red ( middle ),and blue ( bottom ) galaxy populations that are photometrically complete to M R c = −
17. TheDGR is calculated for galaxies measured within ( r/r ) ≤ .
4. 24 – Dw a r f - t o - G i an t R a t i o Clustercentric Radius (r/r ) Red+BlueBlueRed
Fig. 2.— DGR as a function of clustercentric radius for the red+blue (filled circles), red(open squares), and blue (open triangles) cluster galaxy populations. 25 – B l ue G a l a xy F r a c t i on B gc (Mpc ) Fig. 3.— f b as a function of cluster richness ( B gc ) for the cluster galaxy population completeto M R c = −
19 (filled circles) and M R c = −
17 (open diamonds). Error bars for the opensymbols are similar to the filled circles and have been omitted for clarity. Galaxies have beenmeasured within a clustercentric radius of ( r/r ) = 0 .
8. 26 – B l ue G a l a xy F r a c t i on Clustercentric Radius (r/r ) DwarfsGiantsGiants+Dwarfs
Fig. 4.— f b as a function of clustercentric radius for the giants (open triangles), dwarfs(open squares), and giants+dwarfs (filled circles) cluster galaxy populations. 27 – B l ue G a l a xy F r a c t i on Absolute R c Magnitude ) < 1.00.4 < (r/r ) < 0.60.2 < (r/r ) < 0.40.0 < (r/r ) < 0.2
Fig. 5.— f b as a function of magnitude ( M R c ) for four radial bins; ( r/r ) ≤ . . < ( r/r ) < . . < ( r/r ) < . . < ( r/r ) < . B l ue G a l a xy F r a c t i on Redshift
Fig. 6.— Redshift distribution of f b for the cluster galaxy population that is 100% pho-tometrically complete to M R c = −
19 (filled circles), M R c = −
17 (open diamonds), and M R c = − . f b has been measured for galaxieswithin a radius of ( r/r ) = 0 ..