Galaxies in clusters

On the relationship between environment and galaxy properties in clusters of galaxies

Héctor J. Martínez, Valeria Coenda & Hernán Muriel
Instituto de Astronomía Teórica y Experimental, IATE, CONICETObservatorio Astronómico, Universidad Nacional de Córdoba,
Laprida 854, X5000BGR, Córdoba, Argentina
July 5, 2019

We study the correlation between different properties of bright () galaxies in clusters and the environment in the Sloan Digital Sky Survey (SDSS). Samples of clusters of galaxies used in this paper are those selected by Coenda & Muriel that are drawn from the Popesso et al. and Koester et al. samples. Galaxies in these clusters have been identified in the Main Galaxy Sample of the Fifth Data Release (DR5) of SDSS. We analyse which galaxy properties correlate best with either, cluster mass or cluster-centric distance using the technique by Blanton et al. We find that galaxy properties do not clearly depend on cluster mass for clusters more massive than . On the other hand, galaxy properties correlate with cluster-centric distance. The property most affected by the cluster-centric distance is colour, closely followed by the colour. These results are irrespective of the cluster selection criteria. The two samples of clusters were identified based on the X-ray emission and the galaxy colours, respectively. Moreover, the parameter that best predicts environment (i.e. cluster-centric distance) is the same found by Martínez & Muriel for groups of galaxies and Blanton at al. for the local density of field galaxies.

galaxies: fundamental parameters – galaxies: clusters: general – galaxies: evolution
pagerange: On the relationship between environment and galaxy properties in clusters of galaxiesReferences

1 Introduction

Galaxy properties depend on environment, the high fraction of early type galaxies in rich cluster being one of the best examples. The fact that this fraction also evolves with time, i.e., an increasing of the S/S0 rate with redshift (Dressler et al., 1997; Fasano et al., 2000), strongly suggests that galaxy morphologies are being altered by the physical processes that act in the cluster environment. Depending on the latter, i.e., low or high local density, different physical mechanisms will affect galaxy properties in different ways. The fact that most galaxy properties are correlated (morphology, luminosity, colours, etc), makes it difficult to know which properties are affected most by the environment. Blanton et al. (2005) developed a test to evaluate which property, or pair of properties, are most predictive of the local density using galaxies in the Sloan SDSS (York et al., 2000). Martínez & Muriel (2006) extended the analysis to galaxies in groups correlating galaxy properties with both, the mass of groups and the position in the system. Blanton et al. (2005) and Martínez & Muriel (2006) found that galaxy colour is the property most predictive of the environment. This is particularly surprising taking into account the significant differences between field and group environments.

In high mass systems, the hot intracluster medium (ICM) becomes important. Mechanisms like ram pressure stripping (Gunn & Gott, 1972) and starvation/strangulation can affect both the gaseous content of galaxies (Abadi et al., 1999) and the star formation history (Fujita & Nagashima, 1999). From the dynamical point of view, high speed encounters between galaxies are more frequent, producing morphological transformations (Moore et al., 1998, 1999). Galaxies can also suffer the stripping of gas and stars due to the interaction with the cluster potential (e.g. Moore et al. 1999). It is not clear which of these, or some other proposed mechanisms, are dominant. The fact that different processes affect different galaxy components or properties, means that the correlation between these properties can also vary with the environment. Therefore, the most predictive galaxy properties could also depend on the type of system considered. Martínez et al. (2002), Martínez & Muriel (2006), Weinmann et al. (2006) and Zandivarez et al. (2006) found a clear dependence between the galaxy properties and the mass of the host group. For high mass clusters, the correlation is not clear. Several studies did not find dependence between the star formation rate or the fraction of early types with masses or velocity dispersions of clusters (see for instance Goto et al. 2003). Nevertheless, Goto (2005) and Margoniner et al. (2001) found indications of a correlation between blue galaxy fraction with the cluster richness. More recently, Hansen et al. (2007) have found that the fraction of red galaxies increases with the cluster mass, although only weakly for cluster more massive than . Popesso et al. (2007) found that a luminous X-ray intracluster medium can affect the colour of galaxies. On the other hand, the local density-morphology (Hubble & Humason, 1931; Oemler, 1974; Dressler, 1980) or the cluster-centric distance-morphology relation (Whitmore & Gilmore, 1991) has been confirmed by several authors in different environments (see Domínguez et al. 2001 for a discussion between these two approaches).

In this paper we systematically explore the ability of different galaxy properties to predict the total mass of the cluster and the normalised cluster-centric distance. We have considered two samples of high mass clusters based on different selection criteria.

This paper is organised as follows: in section 2 we describe the sample of galaxies in clusters; the analyses of the dependence of galaxy properties on mass and on the cluster-centric distance are carried out in sections 3 and 4 respectively. We summarise our results and discuss them in section 5.

Galaxy magnitudes used throughout this paper have been corrected for Galactic extinction using the maps by Schlegel et al. (1998), absolute magnitudes have been computed assuming a flat cosmological model with parameters , and and corrected using the method of Blanton et al. (2003) (KCORRECT version 4.1). All magnitudes are in the AB system.

2 The sample of galaxies in clusters

2.1 The cluster sample

Clusters of galaxies used in this paper has been taken from the cluster catalogue constructed by Coenda & Muriel (2008). This catalogue was drawn from two cluster catalogues based on SDSS: ROSAT-SDSS Galaxy Cluster Survey of Popesso et al. (2004, hereafter P04), which is a X-ray selected cluster sample and the MaxBGC Catalogue of Koester et al. (2007b, hereafter K07), which is an optically selected cluster sample. Briefly, the ROSAT-SDSS catalogue provides X-ray properties of the clusters derived from the ROSAT data, and optical parameters computed from SDSS data. P04 includes clusters with masses from to in the redshift range . On the other hand, the optical MaxBGC catalogue relies on the observation that the galaxy population of rich clusters is dominated by the bright red galaxies tightly clustered in colour (the E/S0 ridgeline). The K07 catalogue comprises galaxy clusters with velocity dispersions and redshifts .

The subsamples from P04 and K07 selected by Coenda & Muriel (2008), labelled as C-P04 and C-K07, comprise galaxy clusters in the redshift range . For the K07 they also applied a restriction in the richness selecting clusters with in order to have cluster masses comparable to those in the P04 sample. To select clusters members and estimate the physical properties of clusters they used the Main Galaxy Sample (MGS; Strauss et al. 2002) of the Fifth Data Release (DR5) of SDSS (Adelman-McCarthy et al., 2007) that is complete down to a Petrosian (1976) magnitude . To identify cluster members, Coenda & Muriel (2008) use the friends-of-friends (fof) algorithm developed by Huchra & Geller (1982) with percolation linking length values according to Díaz et al. (2004). As a result, they get for each field a list of substructures with at least 10 members identified by fof. The second step consists in a eyeball examination of the structures detected by fof, a comparison between them and the listed cluster position and redshift to determine which coordinates and redshift fit best the observed galaxy over-density, i.e., the cluster centre. According to Coenda & Muriel (2008), for of the clusters the angular position of the centre given by fof is better than the original value, whereas for of the clusters the redshift according to fof is a better match to the observed distribution than the listed one. From the redshift distribution of galaxies within the authors determine the line-of-sight extension of the cluster, i.e., a maximum and a minimum redshift for the cluster. They then consider as cluster members all galaxies in the field that lie within that redshift range.

Through visual inspection Coenda & Muriel (2008) classified clusters based on their substructure. For the purposes of this work, we will only consider the subsamples C-P04-I and C-K07-I that comprise regular clusters (type I in Coenda & Muriel 2008) and exclude systems that have two or more close substructures of similar size in the plane of the sky and/or in the redshift distribution.

Once the members of each cluster are selected Coenda & Muriel (2008) compute some cluster physical properties we are interested in. Namely, they compute the line-of-sight velocity dispersion , virial radius and mass and the radius which encloses a mean over-density 200 times the mean density of the universe, .

The mean values of these parameters are shown in table 1 where it can be seen that the subsample drawn from P04 includes on average clusters slightly more massive and larger than the subsample taken from K07. The C-P04-I and C-K07-I galaxy cluster samples comprise 49 and 209 clusters respectively.

[] [] [] []
C-P04-I Sample
C-K07-I Sample
Table 1: Mean values of the cluster physical properties of our cluster samples.
Property Minimum Value Maximum Value
Table 2: Parameters’ cut-offs that define our samples of galaxies in clusters

2.2 Galaxy parameters

As in Martínez & Muriel (2006) and to avoid systematic effects, we have constructed volume limited samples of galaxies instead of using flux limited samples with a galaxy weighting scheme to account for Malmquist bias. This is crucial for a fair comparison of galaxies in clusters at different redshifts. Thus we basically deal with galaxies brighter than and . With this restriction our samples of galaxies comprise: 786 galaxies from the C-P04-I and 3041 from the C-K07-I samples.

Among the available data for each object in the MGS, we have used in our analyses parameters that are related to different physical properties of the galaxies: luminosity, star formation rate, light distribution inside the galaxies and the dominant stellar populations. The galaxy parameters we have focused our study on are:

  1. band absolute magnitude, .

  2. and colours.

  3. The mono-parametric spectral classification based on the eigentemplates expansion of galaxy’s spectrum . This parameter ranges from about for early-type galaxies to for late-type galaxies (Yip et al. 2004).

  4. band surface brightness, , computed inside the radius that encloses 50% of Petrosian flux, .

  5. band concentration parameter defined as the ratio between the radii that enclose 90% and 50% of the Petrosian flux, . Typically, early-type galaxies have , while for late-types (Strateva et al., 2001).

  6. The Sérsic index (taken from Blanton et al. 2005).

It should be noted that in our samples we do not have galaxies with , i.e. greater than the average seeing in SDSS (the average seeing in SDSS is below a conservative value of ), therefore those galaxy parameters that involve any measure of the galaxy size should not be affected by the effect of seeing.

We have introduced some further cut-offs in these galaxy properties besides luminosity, a complete list is shown in Table LABEL:cutoffs. This excludes only a few galaxies from our analyses but is necessary in order to properly bin these quantities in the statistics we perform in next section. It should be mentioned that we do not introduce further cut-offs in the cluster mass nor in the cluster-centric distance. In Figure 1 we show the distributions of galaxy parameters.

Figure 1: The distributions of galaxy properties in our samples. Dashed blue line: C-K07-I sample, continuous red line: C-P04-I sample. We also show the distribution of cluster mass and the distribution of the projected cluster-centric distance in units of .

As can be seen in Table 1 and Figure 1, despite the clusters derived from the Popesso sample are, on average, slightly more massive than those drawn from the Koester sample, galaxy properties in the C-P04-I sample are very similar to those in C-K07-I having the latter a slightly smaller fraction of red galaxies (85% and 89% of galaxies redder than , respectively).

3 Which galaxy property correlates best with the environment?

In order to determine which galaxy properties are more correlated with, either, cluster mass or the projected distance to the centre of the cluster, we perform here the same analysis carried out by Blanton et al. (2005) and Martínez & Muriel (2006). Details of how to compute the quantities and are given in Martínez & Muriel (2006), thus, we will just briefly summarise here their meaning. The galaxy property which correlates best with a quantity that characterises the environment (either, cluster mass or cluster-centric distance in this work), is the one that minimises the variance of after subtracting the global trend of as a function of . That is, the property that minimises the expression:


in which has been binned into bins wide and centred in () and for each of these bins the mean value of is . Clearly, for any property , the quantity will be smaller than the corresponding variance of with no trend subtraction. We label this variance and present all of our results as the difference .

The quantity is independent of the units of the physical quantity , but it can be sensitive to the choice of binning. To have robust results we take care that each bin is larger than the mean errors in the considered parameter, is smaller than the features in the parameter’s distribution, and contains a large enough number of galaxies. It can be straightforwardly generalised to two properties and if one wants to analyse which pair of properties is most closely correlated with mass (Blanton et al., 2005). However we do not attempt to do so in this work since we do not have enough objects to split them into as many bins as would be required for a proper computation of while still obtaining a reliable outcome.

C-K07-I sample C-P04-I sample
Property Significance Significance
50% 45%
49% 49%
46% 44%
48% 47%
53% 49%
45% 41%
49% 48%
Table 3: Galaxy parameters as cluster mass indicators, i.e., in this case (see text for details). Quoted values are expressed in units of . Boxes highlight the lowest values of , i.e., those corresponding to the galaxy parameter that predicts best the mass of the clusters. Quoted significances are assessed using the bootstrap technique as described in the text.

3.1 Cluster mass

Figure 2: The mean value of cluster mass as a function of the galaxy properties considered in our analysis. Error bars are errors in the mean value obtained by the bootstrap re-sampling technique. Shaded areas correspond to the mean value obtained in the bootstrap re-samplings plus/minus 1 error bar.

In this subsection we focus on how the galaxy properties relate to cluster mass. There is evidence that for groups of galaxies, galaxy colour is, among a set of galaxy properties similar to the ones considered here, the property that correlates best with the mass of the system (Martínez & Muriel, 2006; Weinmann et al., 2006). It is interesting to test whether this is also true for more massive systems. We show in figure 2 the mean mass of the clusters as a function of galaxy properties. There are some hints of what we would expect, for instance a redder mean colour of galaxies with increasing mass due to the higher fraction of red galaxies. But the trends are not as well defined and strong as one may think a priori. Shaded areas in figure 2 correspond to the mean value plus/minus 1 standard deviation error bar for 500 bootstrap re-samplings in which we assign to each galaxy the mass of the cluster to which another, randomly chosen, galaxy in the sample belongs to. It is clear that the trends are, with the exception of a few points, contained in the shaded areas. We should keep in mind that we are dealing here with bright galaxies, that in order to have volume limited samples we have only galaxies brighter than . In Table 3 we list the values of the differences for our samples of galaxies in clusters. Results differ from one sample to another. They do not even agree in the single property most predictive of mass, let alone the second or the third.

The analysis always provides a parameter that predicts best a given environment. Nevertheless, it does not mean that this parameter is good in predicting the environment. In order to test the significance of the results quoted in Table 3, we again use the bootstrap re-samplings to compute different values of , that we label as , and obtain a distribution for . We then compute the fraction, , of re-samplings that gave values . The significance level of the measured is then . We list these significance values in Table 3. They confirm what is observed in figure 2, that none of the galaxy properties correlates significantly with cluster mass. This may be interpreted as if bright galaxies in clusters were similar from cluster to cluster irrespective of cluster mass.

Figure 3: The mean value of cluster-centric distance in units of as a function of the galaxy properties considered in our analysis. Error bars are errors in the mean value obtained by the bootstrap re-sampling technique. Shaded areas correspond to the mean value obtained in the bootstrap re-samplings plus/minus 1 error bar.

3.2 Cluster-centric distance

It is well known that spatial segregation occurs in galaxy clusters. We now compute of the galaxies projected distance to the cluster centre (in units of ) as a function of the galaxy properties. For this purpose, we assume that the cluster centres are those determined by Coenda & Muriel (2008) as explained in section 2.1. In Figure 3 we show the mean values of as a function of the galaxy parameters, as well as the corresponding areas defined by the bootstrap re-samplings. In contrast to what we found for the cluster mass in the figure 2, there are clear trends of that show what is well known, earlier galaxies inhabit the inner regions of the clusters. The resulting values for and their significance are quoted in table 4.

Now we do find agreement between the two samples for the parameter that correlates best with cluster-centric distance. For both samples the colour ranks first. The colour is as good as in the C-P04-I sample, while in the C-K07-I sample comes second, but by a small difference. From the third position onwards rankings differ. The significance is greater than 68% only for 3 properties: the two colours and eclass. This result is consistent with Martínez & Muriel (2006), who found that the colour is the single parameter that correlates best with the distance from the centre of the system in groups of galaxies.

In the upper left panel of figure 3 the point corresponding to the highest luminosities considered is well below the shaded area in both samples. We tested whether this is due to the brightest galaxy (BCG) alone by repeating the computations excluding the brightest galaxy of each cluster. In the brightest luminosity bin, 88% and 60% of the galaxies are BCGs for the C-K07-I and the C-P04-I samples, respectively. The resulting trend for the C-K07-I sample is kept almost unchanged, while for the C-P04-I sample the point lifts into the shaded area. However, it is important to take into account that the actual brightest galaxy of each cluster is not always included in the samples (this is explored in detail in Coenda & Muriel 2008).

4 Discussion and conclusions

In this paper we have extended the analyses by Blanton et al. (2005) and Martínez & Muriel (2006) to galaxies in clusters. We analyse which galaxy properties correlate best with environment characterised by either cluster mass or cluster-centric distance. We use a sample of bright () galaxies in clusters of galaxies in SDSS identified by two different criteria: X-ray selected clusters and clusters selected according to their red sequence. The two sub-samples of clusters have some differences in the mean properties. X-ray selected clusters tend to have a slightly higher mean mass and a higher fraction of red galaxies than maxBCG systems.

We find that the properties of bright galaxies do not clearly depend on cluster mass for systems more massive than . Although the mass range of our sample of cluster is not very large, the lack of dependence between mass and galaxy properties can be interpreted in terms of galaxy evolution. For systems with masses between and bright galaxies have experienced the same physical processes and therefore have similar properties. This result is consistent with Hansen et al. (2007). Using clusters and groups identified in SDSS with the MaxBCG finder of K07, these authors find that above a cluster mass the fraction of red galaxies increases weakly with increasing mass. For bright galaxies, the lack of a significant correlation between some galaxy properties such as colour and concentration with halo mass for halo masses above is also present in Weinmann et al. (2006) (their figure 11). To check consistency with their findings we have computed the median of colour and the median of the concentration parameter as a function of mass for our samples. We have found that they are remarkably independent on cluster mass, taking values and , respectively, in fully agreement with the higher mass bins of Weinmann et al. (2006). For lower mass systems, the importance of processes like high speed encounters or those produced by the intra-cluster medium, tends to change faster with mass. Martínez et al. (2002) and Martínez & Muriel (2006) analysed groups of galaxies and found a clear dependence between mass and galaxy properties that tends to flatten as the mass of the systems grows. The higher masses considered by these authors are similar to the lower masses analysed in this work. All these results seem to imply that above a mass clusters have a similar population of bright () galaxies, that arises as a result of the action of the same physical mechanisms with similar relative impact.

On the other hand, we find, as expected, that galaxy properties do correlate with cluster-centric distance. The property most affected by the cluster-centric distance is colour, followed closely by the colour. These results are irrespective of the cluster selection criteria. This is also in agreement with Hansen et al. (2007). Moreover, the parameter that best predicts the cluster-centric distance is the same found by Martínez & Muriel (2006) for groups of galaxies and Blanton et al. (2005) for the local density of field galaxies as the most predictive of environment.

Galaxy parameters considered in this work can be classified into two classes. Those related to the physical properties of stars and those associated with the light distribution. In the first set are colour, absolute magnitude and spectral type. To the second group belong the concentration parameter, the surface brightness and the Sérsic index. Colour and spectral types of galaxies strongly depend on the age and metallicity of the stars as well as the present and recent star formation history. The luminosity of galaxies also depends on the same properties, although in a more indirect way. The fact that from field to massive clusters colour is the most sensitive property of galaxies to the present time environment, suggests that what really matters is the overall evolution of the environment where a galaxy and its progenitors form the stars. Moreover, according to Blanton et al. (2005) and Martínez & Muriel (2006), the other two parameters that appear as one of the pair of properties most predictive of the environment for field and group’s galaxies are the magnitude and the spectral type, both belonging to the first group of parameters.

Among the phenomena that can affect the star formation history, we can mention the suppression or stimulation of the star formation due to interactions with the intergalactic medium or with other galaxies. Also, the past merger history of galaxies can play a fundamental role. In the particular case of the cluster environment, a natural segregation in the colour of galaxies arises as a result of cluster-centric gradient in the age of the stellar population as is observed in numerical simulations (e.g. Gao et al. 2005). Galaxies in the inner regions of clusters would have older stellar populations and therefore would be redder. These galaxies would also have had a longer time to deplete their gas reservoirs thus stopping star formation. In this scenario, the reddening as a function of radius emerges naturally. However, these processes might suffer a saturation resulting in a flattening of the mean colour as a function of mass.

The lack of a significant correlation between environment and the galaxy properties in the second set described above, may imply that phenomena like ram pressure stripping or galaxy harassment would have had a secondary role in the evolution of bright galaxies. Nevertheless, these physical processes would also have an impact in the first set of parameters. For example, ram pressure striping can remove an important fraction of the intragalactic gas producing a reddening of galaxies as a consequence of the reduction of the star formation rate. Numerical simulations show that galaxies can lose a high fraction of the gas after a single passage through the inner regions of a cluster (see for instance Abadi et al. 1999). Therefore, above a certain mass (), galaxies will experience the same physical processes acting with similar relative effectiveness thus producing a saturation in the mass-colour relationship.

C-K07-I sample C-P04-I sample
Property Significance Significance
67% 50%
84% 81%
85% 79%
55% 61%
63% 64%
60% 49%
70% 69%
Table 4: Galaxy parameters as cluster-centric distance indicators, i.e., in this case (see text for details). Quoted values are expressed in units of . Boxes highlight the lowest values of , i.e., those corresponding to the galaxy parameter that predicts best the cluster-centric distance. Quoted significances are assessed using the bootstrap technique as described in the text.


We thank the anonymous referee for comments and suggestions that have improved this paper. HJM acknowledges the support of a Young Researcher’s grant from Agencia Nacional de Promoción Científica y Tecnológica Argentina, PICT 2005/38087. This work has been partially supported with grants from Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina (CONICET) and Secretaría de Ciencia y Tecnología de la Universidad de Córdoba.

Funding for the Sloan Digital Sky Survey (SDSS) has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the U.S. Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society. The SDSS Web site is The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The Participating Institutions are The University of Chicago, Fermilab, the Institute for Advanced Study, the Japan Participation Group, The Johns Hopkins University, the Korean Scientist Group, Los Alamos National Laboratory, the Max Planck Institut für Astronomie (MPIA), the Max Planck Institut für Astrophysik (MPA), New Mexico State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.


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