A Study of Catalogued Nearby Galaxy Clusters in the SDSS-DR4: I. Cluster
  Global Properties
Astronomy & Astrophysics manuscript no. aguerri˙rv c© ESO 2019
August 20, 2019
A Study of Catalogued Nearby Galaxy Clusters in the SDSS-DR4
I. Cluster Global Properties
J. A. L. Aguerri, R. Sánchez-Janssen & C. Muñoz-Tuñón
Instituto de Astrofı́sica de Canarias C/ Vı́a Láctea s/n, 38200 La Laguna, Spain. e-mail: jalfonso@iac.es, ruben@iac.es, cmt@iac.es
Received ; accepted
ABSTRACT
Context. Large surveys as the Sloan Digital Sky Survey have made available large amounts of spectroscopic and photometric data of galaxies,
providing important information for the study of galaxy evolution in dense environments.
Aims. We have selected a sample of 88 nearby (z < 0.1) galaxy clusters from the SDSS-DR4 with redshift information for the cluster members.
In particular, we focus our results on the galaxy morphological distribution, the velocity dispersion profiles, and the fraction of blue galaxies in
clusters.
Methods. Cluster membership was determined using the available velocity information. We have derived global properties for each cluster,
such as their mean recessional velocity, velocity dispersion, and virial radii. Cluster galaxies have been grouped in two families according to
their u − r colours.
Results. The total sample consists of 10865 galaxies. As expected, the highest fraction of galaxies (62%) turned to be early-type (red) ones,
being located at smaller distances from the cluster centre and showing lower velocity dispersions than late-type (blue) ones. The brightest
cluster galaxies are located in the innermost regions and show the smallest velocity dispersions. Early-type galaxies also show constant velocity
dispersion profiles inside the virial radius and a mild decline in the outermost regions. In contrast, late-type galaxies show always decreasing
velocity dispersions profiles. No correlation has been found between the fraction of blue galaxies and cluster global properties,such as cluster
velocity dispersion and galaxy concentration. In contrast, we found correlation between the X-ray luminosity and the fraction of blue galaxies.
Conclusions. These results indicate that early- and late-type galaxies may have had different evolution. Thus, blue galaxies are located in more
anisotropic and radial orbits than early-type ones. Their star formation seems to be independent of the cluster global properties in low
mass clusters, but not for the most massive ones. We consider that it is unlikely that the whole blue population consists of recent arrivals
to the cluster. These observational results suggest that the global environment could be important for driving the evolution of galaxies
in the most massive cluster (σ > 800 km s−1). However, the local environment could play a key role in galaxy evolution for low mass
clusters.
Key words. galaxies: clusters: general
1. Introduction
The large amount of spectroscopic and photometric data ob-
tained during the last years by surveys such as the Sloan Digital
Sky Survey (SDSS; York et al. 2000) or the 2dF Galaxy
Redshift Survey (2dFGRS; Colless et al. 2001) have opened
a new horizon for the study of galaxy evolution, and in partic-
ular in the study of galaxy clusters. It is well known that the
environment plays an important role in the evolution of galax-
ies, and it is one of the keys that a good galaxy evolution the-
ory should address. There are several physical mechanisms, not
present in the field, which can dramatically transform galaxies
in high density environments. Galaxies in clusters can evolve
due to, e.g., dynamical friction, which can slow down the
more massive galaxies, circularise their orbits and enhance
their merger rate (den Hartog & Katgert 1996; Mamon 1992).
Send offprint requests to: J. A. L. Aguerri
Interactions with other galaxies and with the cluster grav-
itational potential can disrupt the outermost regions of the
galaxies and produce galaxy morphological transformations
from late- to early-types (Moore et al. 1996), or even change
massive galaxies into dwarf ones (Mastropietro et al. 2005).
Swept of cold gas produced by ram pressure stripping
(Gunn & Gott 1972 ; Quilis et al. 2000) or swept of the hot
gas reservoirs (Bekki et al. 2002) can alter the star formation
rate (SFR) of galaxies in clusters. But it is still a matter of
debate which of these mechanisms is the main responsible
of the galaxy evolution in galaxy clusters (see Goto 2005).
Nevertheless, it is clear that all of these mechanisms transform
galaxies from late- to early-types, and can produce the different
segregations observed in galaxy clusters.
One of the first segregations discovered in galaxy clus-
ters was the morphological one. The first evidences of such
segregation date from Curtis (1918) and Hubble & Humason
http://arxiv.org/abs/0704.1579v1
2 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
(1931), and was quantified by Oemler (1974) and Melnick
& Sargent (1977). In a thorough work, Dressler (1980)
analysed a sample of 55 nearby galaxy clusters, contain-
ing over 6000 galaxies, and observed that elliptical and
S0 galaxies represent the largest fraction of galaxies lo-
cated in the innermost and denser regions of galaxy clus-
ters. In contrast, the outskirts of the clusters were domi-
nated by spiral galaxies. In more distant clusters the frac-
tion of E galaxies is as large or larger than in low-redshift
clusters, but the S0 fraction is smaller (Dressler et al. 1997;
Fasano et al. 2000). This has been interpreted as an evolution
with redshift, being late-type galaxies transformed into early-
type ones. Segregations in velocity space have also been ob-
served in galaxy clusters. Early observations found that E
and S0 galaxies showed smaller velocity dispersions than spi-
rals and irregulars (Tammann 1972; Melnick & Sargent 1977;
Moss & Dickens 1977). This has also been confirmed by
other authors during the last two decades (Sodre et al. 1989;
Biviano et al. 1992; Andreon et al. 1996; Stein 1997). The data
from the ENACS survey (Katgert et al. 1998) produced a large
sample of galaxies with spectroscopic redshifts and shed more
light to this problem. Thus, Adami et al. (1998) studied a sam-
ple of 2000 galaxies, confirming early findings that the ve-
locity dispersion of galaxies increases along the Hubble se-
quence: E/S0 galaxies show smaller velocity dispersions than
early- and late-type spirals. This segregation was also observed
in the velocity dispersion profiles (VDPs): late-type galaxies
have decreasing VDPs, while E, S0 and early spirals show al-
most flat VDPs (Adami et al. 1998). The different kinematics
shown by the different types of galaxies was analysed in more
detail by Biviano & Katgert (2004) who found that the ve-
locity segregation of the different Hubble types is due to dif-
ferences in orbits. Thus, early-type spirals have isotropic or-
bits, while late-type ones are located in more anisotropic or-
bits. The observed morphological and velocity segregation in
clusters have been usually used to conclude that late-type spi-
ral galaxies in clusters are recent arrivals to the cluster potential
(Stein 1997; Adami et al. 1998).
Star formation in galaxies is also affected by the envi-
ronment. Butcher & Oemler (1984) found that the fraction
of blue galaxies, fb, in clusters is smaller than in the field
and evolves with redshift: more distant clusters show larger
values of fb. This trend was interpreted as an evolutionary
effect of the SFR in galaxy clusters. But the significant
increase of new data has made it clear that the Butcher-Oemler
effect is not only an evolutionary trend. A large scatter
in the values of fb has been observed in narrow redshift
ranges (Smail et al. 1998; Margoniner & de Carvalho 2000;
Goto et al. 2003), which suggests that the variation of fb
is influenced by environmental effects. In the past, many
authors have tried to find correlations of fb with cluster
properties, such as X-ray luminosity (Andreon & Ettori 1999;
Smail et al. 1998; Fairley et al. 2002), luminosity limit
and clustercentric distance (Ellingson et al. 2001;
Goto et al. 2003; De Propris et al. 2004), richness
(Margoniner et al. 2001; De Propris et al. 2004), cluster con-
centration (Butcher & Oemler 1984; De Propris et al. 2004),
presence of substructure (Metevier et al. 2000) or cluster
velocity dispersion (De Propris et al. 2004). Some of these
works found correlations between fb and the cluster envi-
ronment while others did not, being such connection still a
matter of debate. However, these works were usually done
using small and heterogeneous cluster samples (but see e.g.,
De Propris et al. 2004).
Environmental effects have also been invoked in order
to explain the differences between the photometrical compo-
nents of cluster and field spiral galaxies. Thus, it has been
observed that the scale-lengths of the disks of spiral galax-
ies in the Coma cluster are smaller than those of similar
galaxies in the field (Gutiérrez et al. 2004; Aguerri et al. 2004).
Interactions between galaxies or with the cluster potential
can disrupt the disks of spiral galaxies in clusters. They
can be strong enough for transforming bright late-type spi-
ral galaxies in dwarfs (Aguerri et al. 2005a). The disrupted
material would be part of the intracluster light already de-
tected in some nearby galaxy clusters (Arnaboldi et al. 2002;
Arnaboldi et al. 2004 ; Aguerri et al. 2005b) and galaxy groups
(Castro-Rodrı́guez et al. 2003; Aguerri et al. 2006).
The observational results summarised before illustrate the
important role played by environment in galaxy evolution.
They also indicate that late-type and early-type galaxies in
clusters are two different families of objects with differ-
ent properties, which points to different origins or evolution.
Nevertheless, the main mechanisms responsible of this differ-
ent evolution still remain unknown. In the present paper, we
study one of the largest and more homogeneous galaxy cluster
sample available in the literature. We have obtained the cluster
membership, mean velocity, velocity dispersion, virial radius
and positions for a sample of 88 clusters located at z < 0.1.
We have investigated the main properties of a large sample of
early (red) and late (blue) types of galaxies, such as their lo-
cation within the cluster, their mean velocity dispersion, their
VDPs, the LX −σ relation, and the fraction of blue galaxies for
each cluster. This work provides important information about
the properties of galaxies in nearby clusters, which will be
useful in order to put constraints on cosmological models of
cluster formation. This is the first paper of a series in which
we will analyse the properties of the dwarf galaxy population
(Sánchez-Janssen et al. in preparation), substructure in galaxy
clusters (Aguerri et al. in preparation), and composite luminos-
ity function of galaxy clusters (Sánchez-Janssen et al., in prepa-
ration).
The paper is organised as follows. Section 2 shows the dis-
cussion about the galaxy cluster sample. The cluster member-
ship and cluster global parameters are presented in Section 3.
The results obtained about the morphological segregation, ve-
locity dispersion profiles, LX − σ relation, and the fraction of
blue galaxies are given in Sections 4, 5, 6 and 7, respectively.
The discussion and conclusions are presented in Sections 8 and
9, respectively. Throughout this work we have used the cos-
mological parameters: Ho = 75 km s
−1 Mpc−1, Ωm = 0.3 and
ΩΛ = 0.7.
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 3
2. Galaxy cluster Sample
We have used photometric and spectroscopic data of ob-
jects classified as galaxies from the SDSS-DR4, an imag-
ing and spectroscopic survey of a large area in the sky
(York et al. 2000). The imaging survey was carried out
through five broad-band filters, ugriz, spanning the range
from 3000 to 10000 Å, reaching a limiting r-band mag-
nitude ≈ 22.2 with 95% completeness, and covering an
area of 6670 deg2 (Adelman-McCarthy et al. 2006). A se-
ries of pipelines process the imaging data and perfom the
astrometric calibration (Pier et al. 2003), the photometric re-
duction (Lupton et al. 2002) and the photometric calibration
(Hogg et al. 2001). Objects brighter than mr = 17.77 were se-
lected as possible targets for the spectroscopic survey, covering
an area of 4783 deg2 of the sky for the DR4. The spectroscopic
data were obtained with optical fibers with a diameter of 3
the focal plane, resulting in an spectral covering in the wave-
length range 3800–9200 Å with a resolution of λ/∆λ ≈ 2000.
Our sample consists of all clusters with known redshift at
z < 0.1 from the catalogues of Abell et al. (1989), Zwicky et
al. (1961), Böhringer et al.(2000) and Voges et al. (1999) that
have been mapped by the SDSS-DR4. We downloaded only
those galaxies located within a radius of 4.5 Mpc around the
centres of the galaxy clusters. Only those clusters with more
than 30 galaxies with spectroscopic data in the searching radius
were considered, resulting in a sample formed by 240 clusters
following the previous criteria. The SDSS-DR4 spectroscopic
galaxy target selection was done by an automatic algorithm
(see Strauss et al. 2002). The main galaxy sample consists of
galaxies with r-band Petrosian magnitudes brighter than 17.77
and r-band Petrosian half-light surface brightness brighter than
24.5 mag arcsec−2. The completeness of this sample is high,
exceeding 99% (see Strauss et al. 2002). However, some of
the selected spectroscopic targets were not observed at the end.
This incompleteness has several causes, including the fact that
two spectroscopic fibers cannot be placed closer than 55
given plate, possible gaps between the plates, fibers that fall out
of their holes, and so on. According to these reasons, we expect
that the incompleteness of the spectroscopic data will be more
important for bright galaxies in high density environments such
as galaxy clusters. Figure 1 shows the mean completeness1 of
the SDSS-DR4 spectroscopic data as a function of the r-band
magnitude for the selected galaxies, where a fast increment to-
wards faint magnitudes can be observed. In order to avoid pos-
sible effects on the results due to this effect, we have completed
the spectroscopic SDSS-DR4 observations with the data avail-
able at the Nasa Extragalactic Database (NED). Figure 1 also
shows the mean completeness as a function of r-band magni-
tude after the spectroscopic data from NED were included in
the sample. Notice that the new mean completeness is almost
constant (≈ 85%) for all magnitudes brighter than mr = 17.77.
We have made a second selection of the clusters by considering
only those from our original list with completeness larger than
70% for galaxies brighter than 17.77 in the r-band.
1 We have defined the spectroscopic completeness per magnitude
bin as the ratio of the number of galaxies with spectroscopic data to
the number of galaxies with photometric information.
Fig. 1. Mean completeness of the cluster sample as a function
of the r-band magnitude. Diamonds represent the spectroscopic
data from SDSS-DR4 and black circles after the completion
with data from NED.
3. Cluster Membership
Clusters properties such as the mean cluster velocity, the ve-
locity dispersion, the cluster centre or the virial radius can
be significantly affected by projection effects. Several methods
have been developed during decades in order to obtain reliable
galaxy cluster membership and avoid the presence of interlop-
ers. They can be classified in two families. First, those algo-
rithms that use only the information in the velocity space, e.g.
3σ-clipping techniques (Yahil & Vidal 1977), gapping proce-
dures (Beers et al. 1990; Zabludoff et al. 1990, hereafter ZHG
algorithm) or the KMM algorithm (Ashman et al. 1994). The
other family corresponds to those algorithms which use infor-
mation of both position and velocity, such as the methods de-
signed by Fadda et al. (1996), den Hartog & Katgert (1996), or
Rines et al. (2003).
The cluster membership in our sample was obtained using
a combination of two algorithms. A first rough cluster mem-
bership determination was obtained using the ZHG algorithm,
which in a second step was then refined using the KMM al-
gorithm. The ZHG algorithm is a typical gapping procedure
which determines the cluster membership by the exclusion of
those galaxies located at more than a certain velocity distance
(∆v) from the nearest galaxy in the velocity space. Then, the
mean velocity (vm) and velocity dispersion (σ) of the remain-
ing galaxies are calculated. After sorting objects with velocities
greater than vm, any galaxy separated in velocity more than σ
from the previous one is classified as non member. The same is
done for those galaxies with velocities less than vm. The pro-
cess is repeated several times and finally the mean cluster ve-
locity (vc) and the cluster velocity dispersion (σc) are obtained.
Zabludoff et al. (1990) pointed out that this method lacks statis-
tical rigour and tends to give overestimated values of σc. One
of the disadvantages of this method is that the results obtained
strongly depend on the chosen value of ∆v. Large values of ∆v
imply that a large fraction of interlopers are identified as clus-
ter members. On the contrary, small values of ∆v result in the
4 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
lost of cluster galaxies. We have investigated the variation of
σc for different values of ∆v, obtaining that ∆v=500 km s
is an appropriate value for our clusters. This method has also
the advantage that has an easy implementation and does not re-
quire too much computational time. Recently, it has been used
in works involving a large number of clusters, such as those
from the 2dFGRS (De Propris et al. 2003). The ZHG algorithm
splits the velocity histograms in different galaxy groups, be-
ing one of them located at the catalogued redshift of the clus-
ter. That group was taken and analysed in more detail with the
KMM algorithm. In the few cases where there was no galaxy
group located at the catalogued redshift we identified the most
significant groups having z < 0.1 as the cluster itself.
The KMM algorithm (Ashman et al. 1994) estimates the
statistical significance of bi-modality in a dataset. We have run
it to the group of galaxies given by the ZHG algorithm which
contains the catalogued redshift of the cluster. The KMM algo-
rithm gives us the compatibility of the velocity distribution of
such group of galaxies with a single or multiple Gaussian dis-
tribution. We considered three different cases which are sum-
marised in Fig. 2:
– Single cluster: the velocity distribution of the galaxies is
compatible with a single Gaussian, e.g. Abell 757.
– Cluster with substructure: the velocity distribution is com-
patible with multiple groups. We identified the cluster as
the group with the largest number of galaxies plus those
groups which mean velocities lie within 3σ from the mean
velocity of the largest one2, e.g. Abell 1003.
– Cluster with contamination: the velocity distribution is
compatible with the presence of several groups, but the
mean velocities of the smaller groups deviate more than
3σ from the most populated one, which we identify as the
cluster itself, e.g. Abell 168 .
We have explored the differences in the values of vc and σc
if we consider as interlopers those groups of galaxies located at
a velocity distance larger than 1σ or 3σ from the mean velocity
of the main galaxy group. We obtained that the differences in vc
and σc in 90% of the clusters are less than 20%. The remaining
10% of the clusters are those with significant structure in the
velocity distribution, being most of them more than one cluster
along the line of sight. Thus, we have adopted 3σ as the default
except for those clusters with significant differences between
1σ and 3σ, for which we have measured the mean velocity and
velocity dispersion of the cluster adopting the criteria of 1σ.
Through all of this process, the determination of vc and σc was
done using the biweight robust estimator of Beers et al. (1990).
3.1. Cluster global parameters
Once the cluster membership was determined, we obtained the
global parameters of each cluster, i.e., mean velocity (vc), ve-
locity dispersion (σc), cluster centre, and the radius r200. All of
these parameters were computed using only the cluster mem-
bers.
2 In this case σ is the velocity dispersion of the largest group of
galaxies.
Fig. 2. Velocity histograms of three representative clusters of
the sample. The vertical full lines represent the mean velocity
of the different groups of galaxies in which KMM algorithm
has divided the velocity histogram. The dotted vertical lines
represent vc ± 3σc.
The determination of the cluster centre is important in or-
der to accurately compute the other parameters of the clusters.
The centre of the cluster is determined by the potential well,
which can be traced by the position of the peak of the X-ray lu-
minosity of the cluster. That peak was considered as the centre
of those clusters from our sample with X-ray measurements in
the literature. Unfortunately, not all the clusters from the sam-
ple have X-ray data. In that case, the centre of these clusters
was determined by the peak of the galaxy surface density3. For
those clusters with X-ray data we have compared the centres
given by the peaks of X-ray luminosity and galaxy surface den-
sity, obtaining a mean difference of 150 kpc.
Analytic models (Gott 1972) and simulations
(Cole & Lacey 1996) indicate that the virialized mass of
clusters is generally contained inside the surface where the
mean inner density is 200ρc, where ρc is the critical density
of the Universe. The radius of that surface is called r200.
We have computed the r200 for our clusters using the same
approximation as Carlberg et al. (1997):
r200 =
10 H(zc)
, (1)
where H(zc) is the Hubble constant at the cluster redshift
3 The galaxy surface density was computed using the algorithm de-
signed by Pisani (1996).
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 5
The previous global parameters of the clusters (vc, σc, r200
and centre) were obtained as described above but in a recur-
rent way. In a first step, they where determined using all cluster
member galaxies around 4.5 Mpc from the centre of the clus-
ter. After this step we recalculated the parameters using only
those galaxies located inside r200. The method was repeated
several times until the difference in the parameters obtained in
two consecutive steps was less than 5%. Three or four iterations
were usually enough for reaching the convergence. In order to
obtain reliable parameters of the clusters, those with less than
15 galaxies within r200 were removed from our list. This re-
sults in a final sample formed by 110 nearby galaxy clusters.
Table 1 shows the sample of galaxy clusters and their global
parameters. The columns of Table 1 represent: (1) galaxy clus-
ter name, (2, 3) cluster centres (α (J2000), δ (J2000)), (4) mean
radial velocity, (5) cluster velocity dispersion, (6) r200 radius,
(7) number of galaxies within r200, and (8) spectroscopic com-
pleteness.
For 6 clusters (Abell 1003, Abell 1032, Abell 1459, Abell
2023, Abell 2241 and ZwCl1316.4-0044) large differences in
the mean recessional velocity have been found between the val-
ues given in Table 2 and those from NED. These are the clusters
with no significant galaxy group at the catalogued redshift (see
Section 3).
In order to consider the possible influence of neighbouring
clusters on the global properties of the sample we searched in
the surroundings of each cluster for the presence of compan-
ions. Following Biviano & Girardi (2003), we have considered
that two clusters, i and j, are in interaction when:
|vi − vj| < 3(σi + σj) Ri,j < 2(r200,i + r200,j), (2)
where Ri, j is the projected distance between the centres of
the clusters and vi, j, σi, j, r200,i, j their respective mean velocities,
velocity dispersions and r200. We found 16 couples of clusters
in interaction according to the previous criteria. The remaining
sample (88 clusters) followed the isolation criteria, and will be
used in the analysis presented in the following sections. Figure
3 shows the sky distribution of the cluster members and the
galaxy velocities as a function of clustercentric distance for a
sample of 8 clusters. Red points represent the galaxies taken as
cluster members while black points are interlopers. Notice the
large number of interlopers in some of the galaxy clusters, such
as Abell 1291, Abell 1383, Abell 2244. Some of them, Abell
1291 and Abell 1383, were not included in the final isolated
sample due to the presence of companions.
3.2. Corrections to line-of-sight velocities
Line-of-sight velocities of galaxies in clusters were corrected
by two effects: cosmological redshift and global velocity field.
We should take into account that we will compare the veloc-
ity dispersion of clusters located at different redshifts. Thus,
for each galaxy we have 1 + zobs = (1 + zc)(1 + zgal)
(Danese et al. 1980), being zobs the apparent redshift of the
galaxies, zc the cosmological redshift of the cluster, and zgal
the redshift of the galaxy respect to the cluster centre. This cor-
rection can affect up to 10% for the most distant clusters in our
sample.
Galaxy clusters are frequently part of larger cosmological
structures such as filaments, superclusters or multiple systems,
which can affect the velocity field resulting in a modified clus-
ter velocity dispersion. The interaction between galaxy clus-
ters can also produce distorted velocity fields. We have inves-
tigated the importance of these effects in the velocity field of
our clusters by making a least-square fit to the radial velocities
of cluster galaxies with respect to their position in the plane of
the sky (see den Hartog & Katgert 1996; Girardi et al. 1996).
For each fit we computed the coefficient of multiple determi-
nation, R2. In order to test the significance of the fitted veloc-
ity gradients, we run 1000 Monte Carlo simulations for each
cluster for which the correlation between position and veloc-
ity was removed. This was achieved by shuffling the veloc-
ities of the galaxies with respect to their positions. We de-
fined the significance of velocity gradients as the fraction of
Monte Carlo simulations with R2 smaller than the observed
one. This correction of the velocity field was applied to those
cluster in which the significance of velocity gradients is larger
than 99% ( 30% of the total sample). However, this correction
has small effects both in the shape of the velocity dispersion
profiles and on the total velocity dispersion (the mean abso-
lute correction was about 40 ± 15 km s−1). This is in agree-
ment with similar corrections applied in other cluster samples
(den Hartog & Katgert 1996; Girardi et al. 1996).
3.3. Comparison with other methods
Some of the clusters presented in our sample have been previ-
ously studied by other authors. However, we have avoided com-
paring our results with those from the literature given the differ-
ent datasets used. In order to compare our cluster membership
method with others proposed in the literature, we have com-
puted σc of our clusters with two more methods: a 3σ-clipping
and the method proposed by Fadda et al. (1996). The median
absolute difference between our σc and those computed by the
3σ-clipping method is only 17 km s−1. Only 10% of the clus-
ters show important diferences (∆σc > 200 km s
−1) in the com-
putation of the velocity dispersion of the cluster with the two
methods. They correspond to those clusters affected by large
amount of structure along the line of sight. The 3σ-clipping
method gives for these clusters considerably larger values of
σc than ours. Differences were larger when we compared with
Fadda’s method. In this case the mean absolute difference in
σc between the two methods was 84 km s
−1 and 80% of the
clusters show differences smaller than 200 km s−1.
Recently, Popesso et al. (2006) have obtained the values of
σc for a sample of Abell clusters using SDSS-DR4 data, for
which cluster membership was obtained using the selection al-
gorithm of Katgert et al. (2004). The median absolute differ-
ence between our and their σc is 45 km s
−1 for the 28 clusters
in common. Only for 4 clusters (Abell 1750, Abell 1773, Abell
2244 and Abell 2255) the absolute differences in σc is larger
than 200 km s−1.
We have also compared our results with those given in the
cluster catalogue presented by Miller et al. (2005). We found 16
clusters in common, being 74 km s−1 the median absolute dif-
6 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
Fig. 3. Galaxy surface density (images) and radial velocity versus distance to the cluster center for the galaxy cluster member
(red points) of a subsample of 8 clusters. The overplotted circle have a radius equal to r200 for each cluster. The black points
represent interloper galaxies.
ference between our and their σc. In this case, 3 clusters show
an absolute differences in σc larger than 200 km s
We can conclude that in most of the cases our cluster mem-
bership method reported values of σc similar to those given by
other methods. Only for 10-20% of the clusters the absolute
differences in σc between our method and the others is larger
than 200 km s−1. For these clusters the structure along the line
of sight is the responsible of the difference, being our σc values
smaller than the others.
3.4. Lx-σ relation
We can learn about the nature of cluster assembly by studying
the relations between cluster observables. One of the most uni-
versals is the well known relation between the cluster X-ray lu-
minosity and the velocity dispersion of its galaxies (LX ∝ σbc).
Cluster formation models predict that if the only energy source
in the cluster comes from the gravitational collapse, then b ≈ 4.
This relation has been studied in the literature by many authors
using different cluster samples, finding values of b between
2.9 and 5.3 (Edge & Stewart 1991; Quintana & Melnick 1982;
Mulchaey & Zabludoff 1998; Mahdavi & Geller 2001;
Girardi & Mezzetti 2001; Borgani et al. 1999;
Xue & Wu 2000; Ortiz-Gil et al. 2004; Hilton et al. 2005).
The study of the LX − σc relation in our cluster sample will
be also useful as another check for the values of σc we have
derived. We have X-ray data for 48 galaxy clusters from our
sample. The X-ray data have been obtained from Ebeling et
al. (1998), Böhringer et al.(2000), Ebeling et al.(2000) and
Ledlow et al.(2003), and the X-ray luminosities are measured
in the ROSAT band (0.1-2.4 keV).
Figure 4 shows the LX−σ relation for this subset with avail-
able X-ray data in the literature. The Spearman coefficient of
the relation is 0.56 and the significance from zero correlation
is greater than 3σ. This indicates the existence of a correlation
between LX and σc for the clusters of our sample. We used the
bivariate correlated errors and intrinsic scatter (BCES) bisector
method of Akritas & Bershady (1996) to obtain the coefficient
and power-law slope estimates of the relation. This fitting tech-
nique takes into account errors in both variables and intrinsic
scatter. The LX − σc relation for our clusters is given by:
LX(0.1 − 2.4 keV) = 1033.7±1.2σ3.9±0.4 (3)
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 7
Fig. 4. LX−σ relation for the 48 galaxy clusters with X-ray data
in the ROSAT band (0.1-2.4 keV) from our sample. The full
line represents the best fit using the BCES bisector algorithm
(see text for more details).
Fig. 5. Absolute r-band magnitude as a function of redshift for
the galaxies of our cluster sample.
This result is in very good agreement with another mea-
surement of this relation using the same ROSAT band (0.1-2.4
keV) for the X-ray data and the same fitting algorithm (see
Hilton et al. 2005).
3.5. Redshift distribution and sample completeness
The 88 isolated galaxy clusters are located in a redshift range
between 0.02 and 0.1, with an average redshift of 0.071. Figure
5 shows the absolute r-band magnitude (Mr) as a function
of the redshift for the galaxies in our cluster sample4. It is
clear that the completeness magnitude is a function of redshift.
This figure shows that the full sample is complete for galaxies
brighter than Mr = −20.0. The lack of completeness for fainter
galaxies will be taken into account in the subsequent analysis.
4 See section 3 for the explanation of the computation of the abso-
lute magnitudes of the galaxies.
4. Morphological Segregation
Light concentration or colours have been used extensively in
the literature in order to classify galaxies. Shimasaku et al.
(2001) and Strateva et al. (2001) using SDSS data, found that
the ratio of Petrosian 50 percent light radius to Petrosian 90
percent light radius, Cin, measured in the r-band image was a
useful index for quantifying galaxy morphology. Strateva et al.
(2001) also found that the colour u − r = -2.22 efficiently sep-
arates early- and late-type galaxies at z < 0.4. We have used
colours for classifying galaxies, because properties such as ve-
locity dispersion in galaxy clusters are better correlated with
galaxy colours than galaxy morphology (Goto 2005). The mag-
nitude of the galaxies were corrected by two effects: Galactic
absorption and k−correction. The Galactic absorption in the
different filters was obtained from the dust maps of Schlegel
et al. (1998). We applied the k−correction using the kcor-
rect.v4 1 4 code by Blanton et al. (2003) in order to obtain the
rest-frame magnitudes of the galaxies for the different band-
passes. Once these two corrections were done, we classified
the galaxies in red (u − r ≥ 2.22) and blue ones (u − r < 2.22).
The galaxy data was downloaded from the SDSS database
according to a metric criteria: we downloaded the information
of all galaxies located within a radius of 4.5 Mpc at each galaxy
cluster redshift. This means that we are mapping different phys-
ical regions for each cluster. In order to avoid this problem we
have studied the ratio rmax/r200 for each cluster, being rmax the
maximum distance of a galaxy from the cluster centre for each
galaxy cluster. We have obtained that all clusters of our sample
reach rmaxr200 = 2, and 50% of them reach
Our sample of galaxies consists of 6880 galaxies located
within a radius 2 × r200, being 62% of them red galaxies and
38% blue ones. If we consider all galaxies within 5 × r200 then
the sample has 10865 galaxies, being 55% and 45% red and
blue galaxies, respectively. The red and blue galaxies were also
grouped in three categories according to their r-band magni-
tude: Mr < M
r − 1, M
r − 1 < Mr < M
r + 1, and Mr > M
r + 1
The first group contains the brightest members of the clusters,
the third group contains the so-called dwarf population and the
second one is formed by normal bright galaxies. Table 2 shows
the median location, r-band absolute magnitude, velocity dis-
persion and local density6 of the different galaxy groups. In
general, red galaxies are brighter than blue ones, and are also
located closer to the cluster centre at higher local density re-
gions. The two families of galaxies present different kinemat-
ics, in the sense that red galaxies show a smaller velocity dis-
persion than blue ones. This different kinematic between red
and blue galaxies has also been seen in other studies, and have
been interpreted as red and blue galaxies having different kind
of orbits, being the orbits of blue galaxies more anisotropic
than the red ones (Adami et al. 1998; Biviano & Katgert 2004).
Other authors interpret this difference in velocity dispersion as
an evidence that ram pressure is not playing an important role
in galaxy evolution in clusters. In contrast, tidal interactions
5 M∗r − 5log(h) = −20.04, Blanton et al. (2005)
6 The local surface density (Σ) was computed with the 10 nearest
neighbours to each galaxy belonging to the cluster.
8 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
should be the dominant mechanism (Goto 2005). All of these
properties are independent of the sampled area.
It is also interesting that red dwarf galaxies are located
at similar environments as the brightest red ones: close to
the cluster centre in high local density regions (see also
Hogg et al. 2004). But the red dwarf population shows a larger
velocity dispersion than the brightest red galaxies. Biviano &
Katgert (2004) found that the brightest cluster members were
not in equilibrium with the cluster potential. They are especial
galaxies that could have formed close to the cluster centre or
have fallen to this region due to dynamical friction. In contrast,
dynamical friction is not so efficient in the dwarf population, so
that the main presence of these galaxies in the central regions of
the clusters should be due to their origin. The discussion about
the properties and origin of the dwarf population will be given
in another paper (Sánchez-Janssen et al., in preparation).
5. Velocity Dispersion Profiles
The adopted cluster velocity dispersion was calculated with the
galaxies located within the r200 radius of each cluster. But, how
does σ depend on the clustercentric distance in our sample?.
This can be answered by studying the integrated velocity dis-
persion profiles (VDPs) of the clusters. These profiles also pro-
vide information about the dynamical properties of the galax-
ies. Thus, a system with galaxies predominantly in radial or-
bits produces an outwards declining VDP, while the opposite
behaviour suggests instead that the galactic orbits are largely
circular. In contrast, constant VDPs are characteristic of an
isotropic distribution of velocities (Solanes et al. 2001). Figure
6 shows the VDPs for some of the clusters in our sample. They
show the velocity dispersion of the cluster at a given radius
evaluated using all the galaxies within that radius, without any
restriction in their luminosities. The errors showed in Fig. 6
were computed using the approximation given by Danese et al.
(1980).
In order to classify the VDPs of our clusters, we computed
the velocity dispersion (σi, i = 1, 2, 3, 4, 5) of the galaxies in the
clusters located within five different radius: 0.4×r200, 0.6×r200,
2×r200, 3×r200 and 4×r200, respectively. We compared these
values with σc, given in Table 1. The resulting mean ratios
σi/σc were: 1.02± 0.04, 1.01±0.01, 0.97±0.01, 0.94±0.02 and
0.94±0.02, for i = 1, 2, 3, 4, 5, respectively. These values indi-
cate that within r200 the VDPs of the total galaxy cluster popu-
lation are consistent with being flat. The mean variation of the
VDPs inside r200 is only 2%. The values of σi/σc, i = 3, 4, 5
show that, outside r200 the VDPs slowly decrease. The mean
variation of the VDPs outside r200 is −6%. No differences in the
ratios σi/σc have been found when we have divided the galaxy
sample between bright (Mr < M
r +1) and dwarf (Mr > M
r +1)
galaxies. This flat behaviour of the VDPs inside r200 suggests
that galaxies in these areas have an isotropic distribution of ve-
locities. In contrast, the decline with radius of VDPs outside
r200 points to radial orbits (Solanes et al. 2001).
Figure 6 also shows the VDPs of early- (red) and late-type
(blue) galaxies. In most profiles the velocity dispersion of blue
galaxies is larger than the corresponding one for early-type
ones. We have also analysed the shape of VDPs of blue and
Fig. 7. Histograms of the ratios σi/σc, i = 1, 2, 3, 4, 5 for the
galaxies in the clusters. The black full line represent all galax-
ies, the blue and red lines correspond to late- and early-type
ones. See text for more details.
red galaxies as we did for the total sample. For red galaxies,
we obtained that σi/σc,r are 1.04± 0.03, 1.03±0.03, 0.97±0.02,
0.96±0.02 and 0.96±0.03, for i = 1, 2, 3, 4, 5, respectively.
The values of σi/σc,b for the blue galaxies are: 1.15± 0.07,
1.04±0.03, 0.95±0.04 and 0.93±0.04 and 0.92±0.04, respec-
tively. In those computations, σc,r and σc,b represent the ve-
locity dispersion of the red and blue galaxies within a radius
equal to r200, respectively. Figure 7 show the distribution of
σi/σc, i = 1, 2, 3, 4, 5 for the blue, red and the total galaxy sam-
The VDPs have been studied in the literature by sev-
eral authors. Most of them conclude that for large radii
(r > 1 Mpc) the VDPs are flat (Girardi & Mezzetti 2001;
Rines & Diaferio 2006; Fadda et al. 1996; Muriel et al. 2002).
This is consistent with the mild decrease that we have found
in our clusters. The VDPs for red galaxies in our sample are
almost flat outside r200. This is not the case of the VDPs of
blue galaxies which clearly decrease with radius outside r200.
In the inner regions (r < r200) the VDPs of the total sample
and those corresponding to the red galaxies are flat. In con-
trast, the VDPs of blue galaxies decrease with radius. Different
authors show that VDPs can decrease or increase with radius.
den Hartog & Katgert (1996) made a thorough study and found
that the variations of the VDPs in the innermost regions of clus-
ters (r < 0.5 Mpc) are real and not due to noise or bad centre
election. We have re-computedσ1/σc andσ2/σc only for those
clusters with X-ray centres, and our results did not significantly
change. Thus, we can conclude that in our galaxy cluster sam-
ple only blue galaxies show increasing VDPs towards the cen-
tre of the cluster, while red galaxies show flat VDPs.
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 9
Table 1. Main properties of the different types of galaxies
Galaxies within r/r200 < 5 < r/r200 > < Mr > < σ > < log(Σ) > Ngal
u − r < 2.22 1.85±0.02 -19.65±0.01 1.04±0.01 0.46±0.08 4937
u − r < 2.22 & Mr < M∗r − 1 1.79±0.11 -21.54±0.02 1.08±0.09 0.40±0.25 94
u − r < 2.22 & M∗r − 1 < Mr < M∗r + 1 1.90±0.02 -20.00±0.01 1.03±0.01 0.38±0.08 3126
u − r < 2.22 & Mr > M∗r + 1 1.74±0.03 -18.74±0.02 1.05±0.02 0.64±0.14 1717
u − r ≥ 2.22 1.03±0.01 -20.16±0.01 0.90±0.01 0.80±0.05 5928
u − r ≥ 2.22 & Mr < M∗r − 1 0.95±0.05 -21.62±0.02 0.78±0.03 0.89±0.14 537
u − r ≥ 2.22 & M∗r − 1 < Mr < M
r + 1 1.10±0.02 -20.20±0.01 0.91±0.01 0.75±0.06 4592
u − r ≥ 2.22 & Mr > M∗r + 1 0.85±0.04 -18.91±0.02 0.90±0.03 1.06±0.12 729
Galaxies within r/r200 < 2 < r/r200 > < Mr > < σ > < log(Σ) > Ngal
u − r < 2.22 0.97±0.01 -19.61±0.02 1.08±0.02 0.80±0.07 2636
u − r < 2.22 & Mr < M∗r − 1 1.15±0.07 -21.56±0.03 1.18±0.13 0.62±0.24 54
u − r < 2.22 & M∗r − 1 < Mr < M∗r + 1 1.04±0.01 -19.96±0.01 1.08±0.02 0.70±0.07 1648
u − r < 2.22 & Mr > M∗r + 1 0.85±0.01 -18.70±0.02 1.07±0.03 1.02±0.09 934
u − r ≥ 2.22 0.67±0.01 -20.14±0.01 0.91±0.01 0.98±0.04 4244
u − r ≥ 2.22 & Mr < M∗r − 1 0.57±0.03 -21.65±0.02 0.80±0.03 1.02±0.13 397
u − r ≥ 2.22 & M∗r − 1 < Mr < M
r + 1 0.69±0.01 -20.19±0.01 0.92±0.01 0.94±0.05 3239
u − r ≥ 2.22 & Mr > M∗r + 1 0.61±0.02 -18.92±0.02 0.89±0.03 1.19±0.10 608
The previous findings can also be seen in Fig 8. We show
the VDPs of the different galaxy classes for the normalised
cluster, which was obtained by normalising the scales and ve-
locities of each galaxy of the sample. Thus, the radial distance
of each galaxy to the cluster centre was scaled by r200 of the
corresponding cluster, and the relative velocity of each galaxy
cluster was normalised by the velocity dispersion of the clus-
ter. Figure 8 shows the VDPs which correspond to the total,
bright (Mr < M
r + 1) and dwarf (Mr > M
r + 1) galaxy sam-
ples. We have also distinguished between red and blue objects.
The VDPs of the total galaxy sample indicate that blue galax-
ies have always larger velocity dispersion than red ones. They
also show always decreasing VDPs, while red ones have almost
constant and slowly decrease VDPs inside and outside r200, re-
spectively. These features can also be seen in the VDPs of the
bright galaxy sample. In contrast, red and blue dwarfs show
decreasing VDPs inside r200.
The shape of the VDPs can provide information about
the dynamical state of the galaxies. Thus, clusters with
galaxies predominantly in radial orbits produce an outwards
declining VDP. This is the case of the blue galaxies of
our sample, which is in agreement with previous findings
(Biviano & Katgert 2004; Adami et al. 1998). We have also
obtained that the red dwarf galaxies inside r200 has an outwards
declining VDP. This would imply that this kind of galaxies may
also be located in radial orbits. In contrast, constant VDPs im-
ply an isotropic distribution of velocities (Solanes et al. 2001).
This is the case of the red bright galaxy population inside r200.
6. Fraction of blue galaxies
Butcher & Oemler (1984) observed that the fraction of blue
galaxies ( fb) in clusters evolves with redshift, in the sense that
galaxy clusters located at medium redshift have a larger fb than
nearby ones. This has been usually interpreted as an evolution-
ary trend in clusters. But it is a matter of debate which is the
role played by the environment in the change of the fraction
of blue galaxies. We have computed fb in our sample of galaxy
clusters, studying the variation with z and the possible influence
of the environment.
6.1. Adopted aperture and limiting magnitude
The original analysis of Butcher & Oemler (1984) defined blue
galaxies as those within a radius containing 30 per cent of the
cluster population, being brighter than Mv = −20 and bluer by
0.2 mag in B − V than the colour-magnitude relation defined
by the cluster early-type galaxies. It has been noticed by sev-
eral authors that the fraction of blue galaxies strongly depends
on the magnitude limit and the clustercentric distance used
(Ellingson et al. 2001; Goto et al. 2003; De Propris et al. 2004;
Andreon et al. 2006). They observed that fb grows when the
magnitude limit is fainter and the aperture is larger, reflect-
ing the existence of a large fraction of blue faint galaxies in
the outer regions of the clusters. De Propris et al. (2004) con-
sidered appropriate to measure fb in apertures based on clus-
ter physical properties. They used r200 as the aperture radius
where they measured fb for their clusters. We have adopted
also this radius in order to determine fb in our galaxy clus-
ters. As it was previously commented, fb depends also on
the adopted limiting magnitude of the galaxies in clusters.
It should be noticed that as we move to higher redshifts we
systematically lose faint galaxies (see Fig 2). Our clusters
spread in a redshift range 0.02 < z < 0.1, and only galax-
ies brighter than Mr = −20.0 (≈ M∗r + 0.5) can be observed
at all redshifts. For this reason, we have adopted this abso-
lute magnitude as the limiting magnitude for the computa-
tion of fb. This ensures us to work with a complete galaxy
sample at all redshifts. Other authors adopted fainter limiting
magnitudes, e.g. M∗ + 1.5 (De Propris et al. 2004) or M∗ + 3
(Margoniner & de Carvalho 2000). If there is a large number
of blue galaxies at faint magnitudes, we expect that our val-
ues of fb will be smaller than those reported by the previous
authors.
10 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
Fig. 6. Velocity dispersion profiles of some clusters of our sample. The black symbols represent the velocity dispersion profile
taking into account all types of galaxies. Blue and red symbols represent the velocity dispersion profiles corresponding to blue
and red galaxies, respectively.
6.2. Colour-magnitude diagrams
We determined the g − r versus r colour-magnitude diagrams
for all the clusters in our sample. The colour-magnitude rela-
tion was measured by a robust fitting routine by minimising the
absolute deviation in g−r colour, using only early-type galaxies
located within an aperture of radius equal to r200. The galaxy
types were determined according to the u−r colour and the light
galaxy concentration parameter, Cin. These two criteria allow
us to identify the most reliable sample of E/S0 galaxies (see
Shimasaku et al. 2001; Strateva et al. 2001). Thus, we consid-
ered early-type galaxies those with u − r ≥ 2.22 and Cin < 0.4.
Figure 9 shows the colour-magnitude diagrams of four repre-
sentative galaxy clusters. The colour-magnitude relation fitted
in each case is also overploted. Figure 9 (left column) also
shows the histograms of the colour distribution, marginalised
over the fitted colour-magnitude relation.
The average of the slopes of the colour-magnitude relations
of the early-type galaxies of the clusters is -0.014±0.008. This
slope is within the errors in agreement with the slope obtained
by Gallazzi et al. (2006) for a large sample of galaxies using
SDSS data. It is also in agreement with the average B−R slope
obtained by De Propris et al. (2004) for a sample of galaxy
clusters from 2dFGRS.
6.3. Calculation the blue fraction of galaxies
As we explained before, the blue fraction of galaxies was com-
puted using only those galaxies brighter than Mr = −20 and
located within an aperture of radius r200. In the present study
we only used spectroscopically confirmed galaxy cluster mem-
bers. This should not bias our results, especially due to our high
completeness. Figure 10 presents fb as a function of redshift.
The errors of fb were computed according to the prescription
given by De Propris et al. (2004). We observe no evolution of
fb with redshift, which means that our sample is ideal to study
the effects of the environment on fb.
We have considered three cluster properties (concentration,
velocity dispersion and X-ray luminosity) of each cluster in or-
der to analyse the dependence of fb on the environment. The
concentration parameter was computed following the prescrip-
tion of De Propris et al. (2004), i.e. C = log(r60/r20), where r60
and r20 are the radii containing 60 and 20 per cent of the cluster
galaxies, respectively. The velocity dispersion of the clusters
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 11
Fig. 8. Velocity dispersion profiles of the galaxies of the normalised cluster. The total galaxy population is showed in the top
panel. Bright galaxies (Mr < M
r + 1) are in the middle panel, and the bottom panel shows the VDPs corresponding to the dwarf
galaxy sample (Mr > M
r + 1). The VDP of the total, blue and red galaxy samples are represented by black, blue and red colours,
respectively (see text for more details).
Fig. 10. The fraction of blue galaxies ( fb) as a function of red-
shift of the galaxy clusters.
was taken from Table 1. The X-ray luminosities were obtained
from the literature (Ebeling et al. 1998, Böhringer et al.2000,
Ebeling et al. 2000 and Ledlow et al. 2003), being measured in
the ROSAT band (0.1-2.4 keV). We only found X-ray data for
48 clusters of the sample.
Figure 11 shows the dependence of the fraction of blue
galaxies on concentration, cluster velocity dispersion and X-
ray luminosity. The non-parametric Spearman test returns that
fb has a low correlation with concentration and velocity dis-
persion. The fraction of blue galaxies correlates best with the
velocity dispersion, but the significance of the correlation is
2.6σ. In contrast, the Spearman test shows correlation between
fb and X-ray luminosity, being the significance of this correla-
tion just 3σ. Notice that the points are distributed in the LX − fb
plane following a triangular shape. Clusters with large X-ray
luminosity (LX(0.1 − 2.4keV) > 1045ergs−1) show small frac-
tions of blue galaxies (less than 10%). Nevertheless, those clus-
ters with small X-ray lumisosity show small and large fraction
of blue galaxies. This correlation could indicate that there is a
threshold over which cluster environment can affect the galaxy
colours, and play a role in the galaxy evolution. This means
that, according with our LX −σ relation, the evolution of galax-
ies could be driven by the cluster environment for those clusters
12 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
Fig. 9. Colour-magnitude relation (left) and histograms (right) of marginalised colour distribution for four representative clusters
at different redshifts of our cluster sample. The full line in left panels represent the fitted colour-magnitude relation. The vertical
point lines in right panel represent the blue/red separation in the Butcher-Oemler effect. The red points are the galaxies with
u − r ≥ 2.22 and Cin < 0.4 (see text for more details).
with velocity dispersion larger than σ ≈ 800 km s−1. Recently,
(Popesso et al. 2006) found a similar correlation between LX
and fb for a larger cluster sample. The shape of the our LX − fb
correlation is similar to the correlation between cluster veloc-
ity dispersion and the fraction of [OII] emitters for clusters at
low redshift reported by Poggianti et al. (2006). They found
that clusters with σ > 550 km s−1 have a constant low fraction
(less than 30%) of [OII] emiters. In contrast, those clusters with
smaller σ show large and small fractions of [OII] emiters.
We have recomputed fb taking into account those galax-
ies within an aperture of radius equal to r200 and brighter than
Mr = −19.5. We restricted the analysis only to those clus-
ters with z < 0.05, because our sample is complete down to
Mr = −19.5 in this redshift range. In this case the number of
cluster decreases to 13. We have again studied the correlations
of fb with galaxy concentration, velocity dispersion and X-ray
luminosity, obtaining similar correlations as with the full sam-
7. Discussion
From the study presented in this paper, most of the galaxies
(62%) located in the central regions of galaxy clusters (r/r200 <
2) are early-type galaxies (see section 4). In contrast, the field
population is dominated by late-type galaxies. In the literature
it is also well established that the colour of galaxies in clus-
ters and field is different, an indication of the low star forma-
tion activity found in cluster galaxies (e.g. Balogh et al. 1998;
Lewis et al. 2002; Gómez et al. 2003). These differences in
morphology and stellar content between field and cluster galax-
ies suggest different evolutionary processes. The facts that late-
type galaxies show larger velocity dispersions and are located
at larger distances from the cluster centre than early-type ones
have been interpreted as late-type galaxies being recent ar-
rivals to the cluster potential, forming a non-relaxed group of
galaxies moving in more radial orbits than early-type ones (e.g.
Stein 1997; Adami et al. 1998). As late-type galaxies fall into
the cluster potential and encounter denser environments, they
evolve to early-type ones. The results presented in the present
work are in agreement with previous findings. However, as
pointed out by Goto (2005), this would imply that a large frac-
tion of galaxies (≈ 40% according to our sample) should be
recent arrivals to the cluster, a possibility that seems unlikely.
Goto (2005) concluded that the different observational proper-
ties between red and blue galaxies may indicate which is the
main mechanism driving the evolution of galaxies in clusters.
Gas stripping, mergers and interactions with other galaxies and
with the cluster potential are the main mechanisms which are
able to transform galaxies in clusters, making late-type galax-
ies lose their gas content, stop their star formation, circularise
their orbits and transform their morphology from disk-like ob-
jects to spheroids. All of these mechanisms affect galaxies in
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 13
Fig. 11. The fraction of blue galaxies ( fb) as a function of galaxy distribution (top), cluster velocity dispersion (middle), and
X-ray luminosity (bottom) of the galaxy clusters.
clusters but, can we infer from the observational results which
is the dominant one?.
It should be noted that the different mechanisms of galaxy
evolution have very different time-scales. While gas stripping
has a very short time-scale (≈ 50 Myr, Quilis et al. 2000), the
galaxy infall process can take ≈ 1 Gyr. The different mecha-
nisms also have different underlying physics. Thus, ram pres-
sure stripping is proportional to the density of the intracluster
medium (ICM) and to the square of the velocity of the galaxy.
In contrast, dynamical interactions are more efficient when the
relative velocity of galaxies is smaller (Mamon 1992). This
means that gas stripping is stronger in the cluster centres and
for galaxies with high velocities, while dynamical interactions
should be more efficient for galaxies with smaller velocity dis-
persions. Numerical simulations have shown that most of the
galaxies inside the virial radius have already been through the
cluster core more than once (Mamon et al. 2004). If gas strip-
ping were the main mechanism driving galaxy evolution in
clusters, according to the short time scale of this process, only
few blue (late-type) galaxies should be observed in the central
regions of clusters. Moreover, gas stripping is also stronger in
galaxies with larger velocity dispersion which means that late-
type galaxies should be more affected by this mechanism than
early-type ones. Based on these considerations, Goto (2005)
concluded that gas stripping is not the main responsible mech-
anism driving the evolution of galaxies in clusters. Instead,
galaxies in clusters evolve mainly by dynamical interactions.
We can add to Goto’s discussion that if gas stripping were the
main galactic evolution mechanism in clusters, then the frac-
tion of blue galaxies should depend on the cluster mass as the
temperature and density of the gas increases with the cluster
mass. According to our results, this is true for those clusters
with large X-ray luminosities. In contrast, the cluster environ-
ment is not so important in driving the evolution of galax-
ies in low mass clusters. Thus, gas stripping may not be the
main responsible mechanism transforming late-type to early-
type galaxies in low mass clusters, but could be important in
the most massive ones. This does not mean that gas stripping
is absent in the evolution of galaxies in clusters; some clear ex-
amples of gas stripping have been observed in galaxies in Virgo
(Kenney et al. 2004).
Dynamical interactions include both interactions with the
cluster potential and with other galaxies. These effects can trig-
ger temporary star formation in cluster galaxies (Fujita 1998),
which can be analysed by studying their colour distribution.
These interactions can also disrupt stars from galaxies, form-
ing at the beginning long tidal tails that subsequently will
be diluted and will form the diffuse light observed in some
nearby clusters like Virgo (see Aguerri et al. 2005b, and ref-
erences therein). These effects will be more important in those
galaxies with smaller relative velocities. Fujita (1998) con-
clude that if the tidal effects enhance the SFR in the galaxies,
then the bluest galaxies should be located close to the clus-
ter centre (within ≈ 300 kpc), whereas they should be in the
outer parts of the cluster if the SFR is induced by galaxy-
galaxy encounters. We have investigated the fraction of blue
14 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
galaxies in our clusters located within 300 kpc from the cen-
tre of the cluster. The sample has been divided in bright and
dwarf galaxies (Mr < M
r + 1 and Mr > M
r + 1, respec-
tively). We have obtained that 40% of the blue bright galax-
ies and 30% of the blue dwarf ones are located at smaller dis-
tance than 300 kpc from the cluster center. This means that
tidal interactions with the cluster potential are not the respon-
sible mechanism for the formation of most of the blue galax-
ies in our clusters. The lack of blue galaxies in the central re-
gions of clusters has been observed also in nearby clusters like
Coma (Aguerri et al. 2004) as well as in other distant clusters
(Rakos et al. 1997; Abraham et al. 1996; Balogh et al. 1997).
These evidences indicate that the evolution of galaxies in
clusters could be driven by the cluster environment in the most
massive ones, but galaxies in low mass clusters could mainly
evolve due to the local environment.
8. Conclusions
In the present paper we have analysed the main properties of
the galaxies of one of the largest (10865 galaxies) and homo-
geneous sample presented in the literature. The galaxies have
been grouped in two families according to their u − r colour.
Those galaxies with u − r ≥ 2.22 formed the red (early-type)
family, and those with u − r < 2.22 the blue (late-type) one.
We have derived the position, velocity dispersion, and VDPs of
both families of galaxies, obtaining:
– Within 2×r200, 62% and 38% of the galaxies turned to be
red and blue, respectively.
– The median positions and velocity dispersions are smaller
for red galaxies than for blue ones.
– Bright (Mr < M∗r −1) and dwarf (Mr > M
r +1) red galaxies
are located at smaller distances than the blue ones, sharing
the same cluster environment.
– The brightest cluster members (Mr < −21.0) show smaller
velocity dispersions than the remaining.
– The VDPs of the total galaxy cluster population are con-
stant with radius in the central regions of the clusters
(r < r200), while slowly decrease in the outermost regions
(r ≥ r200). The red galaxy population have also flat VDPs in
the central regions (r < r200). In contrast, the VDPs of blue
galaxies grow towards the cluster centre. In the outer re-
gions (r > r200), the VDPs of red galaxies decline smoothly
with radius, while for blue ones the decrement is faster.
This indicates that the galaxies in the outermost regions of
the clusters are dominated by the blue population, and have
more radial and anisotropic orbits than galaxies in the inner
regions dominated by the red population.
– The fraction of blue galaxies in our cluster sample does not
correlate with cluster global properties, such as the concen-
tration of the galaxy distribution and cluster velocity disper-
sion. However, we found a correlation between the X-ray
luminosity and the fraction of blue galaxies. Those clusters
with LX(0.1 − 2.4keV) > 1045 erg s−1 have a low fraction
of blue galaxies (less than 10%). In contrast, clusters with
low of X-ray luminosity show large and small fractions of
blue galaxies. This could indicate that the star formation in
cluster galaxies may be regulated by global cluster proper-
ties for clusters with LX(0.1 − 2.4keV) > 1045 erg s−1, i.e.
those clusters with σc > 800 km s
All these results are in agreement with previous findings
from other cluster samples, indicating that red and blue galax-
ies have different evolution in galaxy clusters. We have dis-
cussed these results according to the different galaxy transfor-
mation mechanisms presented in galaxy clusters, concluding
the local environment plays a key role in galaxy evolution in
low mass clusters, while the evolution of galaxies in massive
clusters could be driven by the global cluster environment.
Acknowledgements. We wish to thank to the anonymous referee
for useful coments which have improved this manuscript. We
also acknowledge financial support by the Spanish Ministerio de
Ciencia y Tecnologı́a grants AYA2004-08260. We would like also
to thank T. Beers for providing us with copy of his code ROSTAT,
and K. M. Ashman and S. Zepf for making their KMM code
available to us. Funding for the 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, 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 for the Participating Institutions.
The Participating Institutions are the American Museum of Natural
History, Astrophysical Institute Potsdam, University of Basel,
Cambridge University, Case Western Reserve University, University
of Chicago, Drexel University, Fermilab, the Institute for Advanced
Study, the Japan Participation Group, 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. This research has made use of 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.
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Table 2. Cluster characteristics
Name α (J2000) δ (J2000) vc σc r200 Ngal C
(degrees) (degrees) (km s−1) (km s−1) (Mpc)
Abell0085 10.4571 -9.30694 16633+40−29 979
−39 2.10 273 0.93
Abell0117 14.0100 -10.0022 16568+31−42 531
−27 1.19 60 0.88
Abell0152 17.5229 13.9804 17888+67−34 538
−38 1.12 27 0.85
Abell0168 18.7429 0.365833 13534+23−14 578
−28 1.19 106 0.88
Abell0257 27.3396 14.0372 21060+47−21 381
−44 0.81 26 0.94
Abell0602 118.341 29.3717 18202+34−51 834
−61 1.87 78 0.74
Abell0628 122.543 35.2958 25139+24−89 666
−38 1.47 43 0.83
Abell0671 127.121 30.4169 14599+19−33 610
−33 1.42 72 0.89
Abell0690 129.815 28.9033 23689+44−23 395
−24 0.85 22 0.95
Abell0695 130.309 32.4174 20251+46−37 456
−32 1.04 16 0.86
Abell0699 131.236 27.7508 25375+35−49 438
−37 0.91 19 0.73
Abell0724 134.600 38.5137 28134+25−55 433
−32 1.00 29 0.94
Abell0727 134.976 39.4389 28571+54−14 423
−29 0.96 33 0.96
Abell0757 138.277 47.7036 15402+27−36 409
−30 0.84 30 0.85
Abell0779 139.962 33.7714 6921+13−33 336
−21 0.79 57 0.75
Abell0819 143.076 9.68861 22872+39−50 536
−37 1.19 31 0.94
Abell0883 147.822 5.48799 21750+109−30 523
−58 1.17 18 0.91
Abell0971 154.997 40.9925 27809+54−43 816
−61 1.88 40 0.87
Abell0999 155.842 12.8466 9618+10−54 271
−17 0.60 25 0.90
Abell1003 156.235 47.8442 18762+44−84 617
−34 1.37 29 0.94
Abell1016 156.762 10.9780 9629+34−9 259
−17 0.60 25 0.91
Abell1024 157.096 3.76341 22067+40−20 532
−34 1.26 35 0.90
Abell1032 157.547 4.03417 20008+26−24 355
−32 0.77 25 0.89
Abell1035 158.092 40.1817 20270+36−34 575
−45 1.34 49 0.97
Abell1066 159.911 5.17444 20708+4−81 826
−44 1.71 95 0.92
Abell1142 165.229 10.5477 10601+30−25 557
−38 1.33 59 0.88
Abell1149 165.769 7.57833 21479+2−64 352
−31 0.85 26 0.94
Abell1169 166.967 43.9506 17532+24−35 433
−32 0.91 35 0.94
Abell1173 167.328 41.5624 22789+23−69 611
−41 1.33 35 0.95
Abell1189 167.775 1.09899 28860+109−60 807
−88 1.76 41 0.94
Abell1190 167.902 40.8417 22610+17−39 706
−30 1.50 77 0.92
Abell1205 168.328 2.53867 22852+17−53 890
−53 2.04 83 0.84
Abell1215 170.100 4.34280 14747+25−10 214
−39 0.48 17 0.89
Abell1238 170.711 1.09389 22140+9−54 544
−41 1.30 60 0.91
Abell1270 172.366 54.0428 20728+49−36 569
−39 1.31 43 0.99
Abell1291 173.092 55.9783 17144+43−60 720
−36 1.58 45 0.97
Abell1318 173.883 55.0767 17185+54−22 360
−25 0.81 22 0.96
Abell1346 175.304 5.74613 29523+41−25 790
−62 1.69 66 0.85
Abell1377 176.883 55.7597 15378+35−33 671
−37 1.47 69 0.96
Abell1383 176.973 54.7089 17855+38−35 456
−23 1.02 35 0.95
Abell1385 177.017 11.5864 25337+49−44 609
−45 1.34 22 0.86
Abell1390 177.378 12.3034 25101+48−29 483
−41 1.06 27 0.86
Abell1424 179.361 5.12000 22736+33−39 617
−43 1.46 63 0.95
Abell1436 180.095 56.2314 19432+39−77 712
−44 1.51 66 0.96
Abell1452 180.802 51.6642 18609+56−58 533
−30 1.30 18 0.91
Abell1459 181.108 1.88281 6010+37−21 527
−48 1.18 65 0.95
Abell1507 183.766 59.8947 18009+9−70 379
−36 0.85 23 0.91
Abell1516 184.729 5.24731 23019+24−84 720
−43 1.49 60 0.90
Abell1552 187.392 11.7733 26495+64−42 442
−32 0.93 20 0.88
Abell1564 188.720 1.78056 23763+30−64 641
−62 1.51 46 0.91
Abell1616 191.817 55.0006 24882+65−93 565
−45 1.32 29 0.87
Abell1620 192.510 -1.53764 25400+64−38 829
−43 1.76 58 0.88
Abell1630 192.942 4.59694 19458+36−33 444
−37 0.98 30 0.90
18 Aguerri et al.: Global Properties of Nearby Galaxy Clusters
Table 2. continued.
Name α (J2000) δ (J2000) vc σc r200 Ngal C
(degrees) (degrees) (km s−1) (km s−1) (Mpc)
Abell1650 194.672 -1.76417 25138+86−18 790
−47 1.61 63 0.85
Abell1663 195.717 -2.51782 24953+60−20 729
−40 1.54 72 0.88
Abell1692 198.060 -0.976000 25395+51−47 607
−43 1.32 40 0.89
Abell1728 200.876 11.2960 26977+95−22 824
−62 1.88 50 0.70
Abell1750 202.709 -1.86389 26259+19−14 518
−33 1.15 35 0.95
Abell1767 204.024 59.2042 21174+39−32 885
−40 2.05 109 0.94
Abell1773 205.533 2.24805 23544+31−48 481
−31 1.09 32 0.87
Abell1780 206.149 2.86750 23285+35−22 624
−53 1.45 53 0.90
Abell1783 205.848 55.6261 20550+48−11 383
−32 0.94 33 0.94
Abell1809 208.245 5.16139 23815+39−31 737
−47 1.68 89 0.82
Abell1885 213.431 43.6634 26793+33−50 541
−56 1.23 22 0.92
Abell1999 223.522 54.2682 29841+11−57 463
−54 1.07 24 0.92
Abell2018 225.266 47.2831 26246+93−5 635
−37 1.45 39 0.88
Abell2023 227.496 2.98910 27743+48−18 516
−73 1.12 23 0.86
Abell2026 227.106 -0.267500 27188+62−37 747
−49 1.50 43 0.91
Abell2030 227.844 -0.0857717 27399+38−27 495
−45 1.10 38 0.91
Abell2061 230.317 30.6122 23646+24−19 622
−32 1.43 98 0.83
Abell2067 230.780 30.8703 23039+33−34 917
−46 2.19 118 0.85
Abell2092 233.348 31.1475 20000+42−17 458
−30 0.93 41 0.86
Abell2110 234.953 30.7173 29250+94−28 622
−49 1.25 21 0.83
Abell2122 236.259 36.1161 19793+28−42 826
−47 1.80 91 0.92
Abell2124 236.263 36.1172 19783+41−32 826
−47 1.78 90 0.92
Abell2145 240.094 33.2306 26583+80−23 632
−56 1.40 24 0.87
Abell2149 240.350 53.9061 19564+63−44 459
−27 1.01 20 0.85
Abell2169 243.422 49.1261 17286+30−50 521
−33 1.24 40 0.82
Abell2175 245.132 29.8953 28876+61−64 878
−57 1.76 58 0.87
Abell2199 247.154 39.5244 9118+15−30 747
−19 1.77 269 0.92
Abell2241 254.928 32.6161 29403+70−68 806
−62 1.61 37 0.90
Abell2244 255.663 34.0411 28927+50−57 428
−49 0.99 23 0.94
Abell2245 255.640 33.5056 25686+40−49 535
−39 1.28 39 0.95
Abell2255 258.222 64.0653 24052+19−39 883
−35 1.86 184 0.91
Abell2428 334.065 -9.34139 25207+15−68 433
−37 0.95 26 0.87
Abell2670 358.556 -10.4133 22755+29−20 642
−29 1.46 137 0.94
MACSJ0810.3+4216 122.600 42.2733 19193+44−25 505
−35 1.23 32 0.84
MACSJ1440.0+3707 220.011 37.0839 29402+88−46 587
−49 1.31 18 0.91
NSCJ152902+524945 232.309 52.8433 22063+101−7 652
−43 1.4 45 0.89
NSCJ161123+365846 242.854 36.9700 20221+39−25 485
−39 1.17 30 0.86
RBS1385 215.969 40.2619 24544+40−78 419
−36 0.84 16 0.91
RXCJ0137.2-0912 24.3137 -9.20277 12169+27−30 453
−30 0.93 49 0.92
RXCJ0828.6+3025 127.162 30.4280 14630+50−25 628
−33 1.45 76 0.89
RXCJ0953.6+0142 148.393 1.70550 29450+24−63 584
−59 1.30 22 0.96
RXCJ1115.5+5426 168.887 54.4350 20965+35−66 639
−38 1.35 50 0.94
RXCJ1121.7+0249 170.428 2.81840 14807+25−14 567
−41 1.40 73 0.85
RXCJ1351.7+4622 207.940 46.3668 18937+32−40 531
−27 1.16 40 0.94
RXCJ1424.8+0240 216.159 2.75677 16337+44−57 539
−36 1.19 22 0.91
RXJ1017.7-0002 154.452 -0.0595327 19169+44−28 413
−39 0.90 16 0.91
RXJ1022.1+3830 155.583 38.5308 16300+37−38 591
−33 1.35 51 0.82
RXJ1053.7+5450 163.449 54.8500 21623+23−49 665
−45 1.52 46 0.86
WBL238 146.732 54.4183 13995+51−17 602
−30 1.30 44 0.87
WBL518 220.179 3.45305 8141+19−28 454
−20 1.03 103 0.85
ZwCl0027.0-0036 7.31721 -0.183598 17994+18−44 465
−36 0.99 36 0.85
ZwCl0743.5+3110 116.655 31.0136 17419+49−89 694
−45 1.40 29 0.82
ZwCl1207.5+0542 182.578 5.38500 23137+43−36 580
−40 1.20 40 0.91
Aguerri et al.: Global Properties of Nearby Galaxy Clusters 19
Table 2. continued.
Name α (J2000) δ (J2000) vc σc r200 Ngal C
(degrees) (degrees) (km s−1) (km s−1) (Mpc)
ZwCl1215.1+0400 184.422 3.66040 23229+22−40 955
−39 2.17 130 0.90
ZwCl1316.4-0044 199.816 -0.907816 24972+56−31 557
−20 1.16 38 0.87
ZwCl1730.4+5829 261.856 58.4749 8379+26−36 491
−22 1.02 33 0.86
	Introduction
	Galaxy cluster Sample
	Cluster Membership
	Cluster global parameters
	Corrections to line-of-sight velocities
	Comparison with other methods
	Lx- relation
	Redshift distribution and sample completeness
	Morphological Segregation
	Velocity Dispersion Profiles
	Fraction of blue galaxies
	Adopted aperture and limiting magnitude
	Colour-magnitude diagrams
	Calculation the blue fraction of galaxies
	Discussion
	Conclusions
