Draft version November 9, 2018
Preprint typeset using LATEX style emulateapj v. 11/12/01
DIFFUSE OPTICAL LIGHT IN GALAXY CLUSTERS II:
CORRELATIONS WITH CLUSTER PROPERTIES
J.E. Krick 1,2 and R.A. Bernstein 1
jkrick@caltech.edu, rabernst@umich.edu
Draft version November 9, 2018
ABSTRACT
We have measured the flux, profile, color, and substructure in the diffuse intracluster light (ICL) in
a sample of ten galaxy clusters with a range of mass, morphology, redshift, and density. Deep, wide-
field observations for this project were made in two bands at the one meter Swope and 2.5 meter du
Pont telescope at Las Campanas Observatory. Careful attention in reduction and analysis was paid to
the illumination correction, background subtraction, point spread function determination, and galaxy
subtraction. ICL flux is detected in both bands in all ten clusters ranging from 7.6× 1010 to 7.0× 1011
h−170 L�in r and 1.4× 10
10 to 1.2× 1011 h−170 L�in the B−band. These fluxes account for 6 to 22% of the
total cluster light within one quarter of the virial radius in r and 4 to 21% in the B−band. Average ICL
B− r colors range from 1.5 to 2.8 mags when k and evolution corrected to the present epoch. In several
clusters we also detect ICL in group environments near the cluster center and up to 1h−170 Mpc distant
from the cluster center. Our sample, having been selected from the Abell sample, is incomplete in that
it does not include high redshift clusters with low density, low flux, or low mass, and it does not include
low redshift clusters with high flux, mass, or density. This bias makes it difficult to interpret correlations
between ICL flux and cluster properties. Despite this selection bias, we do find that the presence of
a cD galaxy corresponds to both centrally concentrated galaxy profiles and centrally concentrated ICL
profiles. This is consistent with ICL either forming from galaxy interactions at the center, or forming at
earlier times in groups and later combining in the center.
Subject headings: galaxies: clusters: individual (A4059, A3880, A2734, A2556, A4010, A3888, A3984,
A0141, AC114, AC118) — galaxies: evolution — galaxies: interactions — galaxies:
photometry — cosmology: observations
1. introduction
A significant stellar component of galaxy clusters is
found outside of the galaxies. The standard theory of
cluster evolution is one of hierarchical collapse, as time
proceeds, clusters grow in mass through the merging with
other clusters and groups. These mergers as well as inter-
actions within groups and within clusters strip stars out of
their progenitor galaxies. The study of these intracluster
stars can inform hierarchical formation models as well as
tell us something about physical mechanisms involved in
galaxy evolution within clusters.
Paper I of this series (Krick et al. 2006) discusses the
methods of ICL detection and measurement as well as the
results garnered from one cluster in our sample. We re-
fer the reader to that paper and the references therein
for a summary of the history and current status of the
field. This paper presents the remaining nine clusters in
the sample and seeks to answer when and how intracluster
stars are formed by studying the total flux, profile shape,
color, and substructure in the ICL as a function of cluster
mass, redshift, morphology, and density in the sample of
10 clusters. The advantage to having an entire sample of
clusters is to be able to follow evolution in the ICL and
use that as an indicator of cluster evolution.
Strong evolution in the ICL fraction with mass of the
cluster has been predicted in simulations by both Lin
& Mohr (2004) and Murante et al. (2004). If ongoing
stripping processes are dominant, ram pressure stripping
(Abadi et al. 1999) or harassment (Moore et al. 1996), then
high mass clusters should have a higher ICL fraction than
low-mass clusters . If, however, most of the galaxy evolu-
tion happens early on in cluster collapse by galaxy-galaxy
merging, then the ICL should not correlate directly with
current cluster mass.
Because an increase in mass is tied to the age of the
cluster under hierarchical formation, evolution has also
been predicted in the ICL fraction as a function of red-
shift (Willman et al. 2004; Rudick et al. 2006). Again,
if ICL formation is an ongoing process then high redshift
clusters will have a lower ICL fraction than low redshift
clusters. Conversely, if ICL formation happened early on
in cluster formation there will be no correlation of ICL
with redshift.
The stripping of stars (or even the gas to make stars)
to create an intracluster stellar population requires an in-
teraction between their original host galaxy and either an-
other galaxy, the cluster potential, or possibly the hot gas
in the cluster. Because all of these processes require an
interaction, we expect cluster density to be a predictor of
ICL fraction. Cluster density is linked to cluster morphol-
ogy, which implies morphology should also be a predictor
of ICL fraction. Specifically we measure morphology by
the presence or absence of a cD galaxy. cD galaxies are
the results of 2 - 5 times more mergers than the average
cluster galaxy (Dubinski 1998). The added number of in-
teractions that went into forming the cD galaxy will also
mean an increased disruption rate in galaxies therefore
morphological relaxed (dynamically old) clusters should
1 Astronomy Department, University of Michigan, Ann Arbor, MI 48109
2 Spitzer Science Center, Caltech, Pasadena, CA 91125
2 Krick, Bernstein
have a higher ICL flux than dynamically young clusters.
Observations of the color and fractional flux in the ICL
over a sample of clusters with varying redshift and dynam-
ical state will allow us to identify the timescales involved
in ICL formation. If the ICL is the same color as the
cluster galaxies, it is likely to be a remnant from ongoing
interactions in the cluster. If the ICL is redder than the
galaxies it is likely to have been stripped from galaxies at
early times. Stripped stars will passively evolve toward red
colors while the galaxies continue to form stars. If the ICL
is bluer than the galaxies, then some recent star formation
has made its way into the ICL, either from ellipticals with
low metallicity or spirals with younger stellar populations,
or from in situ formation.
While multiple mechanisms are likely to play a role in
the complicated process of formation and evolution of clus-
ters, important constraints can come from ICL measure-
ment in clusters with a wide range of properties. In addi-
tion to directly constraining galaxy evolution mechanisms,
the ICL flux and color is a testable prediction of cosmolog-
ical models. As such it can indirectly be used to examine
the accuracy of the physical inputs to these models.
This paper is structured in the following manner. In §2
we discuss the characteristics of the entire sample. De-
tails of the observations and reduction are presented in §3
and §4 including flat-fielding, sky background subtraction
methods, object detection, and object removal and mask-
ing. In §5 we lists the results for both cluster and ICL
properties including a discussion of each individual clus-
ter. Accuracy limits are discussed in §6. A discussion of
the interesting correlations can be found in §7 followed by
a summary of the conclusions in §8.
Throughout this paper we use H0 = 70km/s/Mpc, ΩM
= 0.3, ΩΛ = 0.7.
2. the sample
The general properties of our sample of ten galaxy clus-
ters have been outlined in paper I; for completeness we
summarize them briefly here. Our choice of the 10 clusters
both minimizes the observational hazards of the galactic
and ecliptic plane, and maximizes the amount of informa-
tion in the literature. All clusters were chosen to have
published X–ray luminosities which guarantees the pres-
ence of a cluster and provides an estimate of the clus-
ter’s mass. The ten chosen clusters are representative
of a wide range in cluster characteristics, namely red-
shift (0.05 < z < 0.3), morphology (3 with no clear
central dominant galaxy, and 7 with a central dominant
galaxy as determined from this survey, §5.1.2, and not
from Bautz Morgan morphological classifications), spatial
projected density (richness class 0 - 3), and X–ray lumi-
nosity (1.9 × 1044 ergs/s < Lx < 22 × 1044 ergs/s). We
discuss results from the literature and this survey for each
individual cluster in order of ascending redshift in §5.
3. observations
The sample is divided into a “low” (0.05 < z < 0.1)
and “high” (0.15 < z < 0.3) redshift range which we have
observed with the 1 meter Swope and 2.5 meter du Pont
telescope respectively. The du Pont observations were dis-
cussed in detail in paper I. The Swope observations follow
a similar observational strategy and data reduction pro-
cess which we outline below. Observational parameters
are listed in Table 2.
We used the 2048 × 3150 “Site#5” CCD with a
3e−/count gain and 7e− readnoise on the Swope telescope.
The pixel scale is 0.435′′/pixel (15µ pixels), so that the full
field of view per exposure is 14.8′ × 22.8′. Data was taken
in two filters, Gunn-r (λ0 = 6550 Å) and B (λ0 = 4300
Å). These filters were selected to provide some color con-
straint on the stellar populations in the ICL by spanning
the 4000Å break at the relevant redshifts, while avoid-
ing flat-fielding difficulties at longer wavelengths and pro-
hibitive sky brightness at shorter wavelengths.
Observing runs occurred on October 20-26, 1998,
September 2-11, 1999, and September 19-30, 2000. All
observing runs took place within eight days of new moon.
A majority of the data were taken under photometric con-
ditions. Those images taken under non-photometric con-
ditions were individually tied to the photometric data (see
discussion in §4. Across all three runs, each cluster was
observed for an average of 5 hours in each band. In addi-
tion to the cluster frames, night sky flats were obtained in
nearby, off-cluster, “blank” regions of the sky with total
exposure times roughly equal to one third of the integra-
tion times on cluster targets. Night sky flats were taken in
all moon conditions. Typical B− and r−band sky levels
during the run were 22.7 and 21.0 mag arcsec−2, respec-
tively.
Cluster images were dithered by one third of the field
of view between exposures. The large overlap from the
dithering pattern gives us ample area for linking back-
ground values from the neighboring cluster images. Ob-
serving the cluster in multiple positions on the chip reduces
large-scale flat-fielding fluctuations upon combination. In-
tegration times were typically 900 seconds in r and 1200
seconds in B.
4. reduction
In order to create mosaiced images of the clusters with
a uniform background level and accurate resolved–source
fluxes, the images were bias and dark subtracted, flat–
fielded, flux calibrated, background–subtracted, extinction
corrected, and registered before combining. Methods for
this are discussed in detail in paper I and summarized be-
The bias level is roughly 270 counts which changed by
approximately 8% throughout the night. This, along with
the large-scale ramping effect in the first 500 columns of ev-
ery row was removed in the standard manner using IRAF
tasks. The mean dark level is 1.6 counts/900s, and there
is some vertical structure in the dark which amounts to
1.4 counts/900s over the whole image. To remove this
large-scale structure from the data images, a combined
dark frame from the whole run was median smoothed over
9× 9 pixels (3.9′′), scaled by the exposure time, and sub-
tracted from the program frames. Small scale variations
were not present in the dark. Pixel–to–pixel sensitivity
variations were corrected in all cluster and night-sky flat
images using nightly, high S/N, median-combined dome
flats with 70,000 – 90,000 total counts. After this step,
a large-scale illumination pattern remains across the chip.
This was removed using night-sky flats of “blank” regions
of the sky, which, when combined using masking and re-
ICL 3
jection, produced an image with no evident residual flux
from sources but has the large scale illumination pattern
intact. The illumination pattern was stable among images
taken during the same moon phase. Program images were
corrected only with night sky flats taken in conditions of
similar moon.
We find that the Site#3 CCD does have an approx-
imately 7% non-linearity over the full range of counts,
which we fit with a second order polynomial and corrected
for in all the data. The same functional fit was found for
both the 1998 and 1999 data, and also applied to the 2000
data. The uncertainty in the linearity correction is incor-
porated in the total photometric uncertainty.
Photometric calibration was performed in the usual
manner using Landolt standards at a range of airmasses.
Extinction was monitored on stars in repeat cluster images
throughout the night. Photometric nights were analyzed
together; solutions were found in each filter for an extinc-
tion coefficient and common magnitude zero-point with a
r− and B−band RMS of 0.04 & 0.03 magnitudes in Oc-
tober 1998, 0.03 & 0.03 magnitudes in September 1999,
and 0.05 & 0.05 magnitudes in September 2000. These
uncertainties are a small contribution to our final error
budget (§5.3). Those exposures taken in non-photometric
conditions were individually tied to the photometric data
using roughly 10 stars well distributed around each frame
to find the effective extinction for that frame. Among
those non-photometric images we find a standard devia-
tion of 0.03 magnitudes within each frame. Two further
problems with using non-photometric data for low surface
brightness measurements are the scattering of light off of
clouds causing a changing background illumination across
the field and secondly the smoothing out of the PSF. We
find no spatial gradient over the individual frame to the
limit discussed in §5.3. The change in PSF is on small
scales and will have no effect on the ICL measurement
(see 4.2.1).
Due to the temporal variations in the background, it is
necessary to link the off-cluster backgrounds from adjacent
frames to create one single background of zero counts for
the entire cluster mosaic before averaging together frames.
To determine the background on each individual frame we
measure average counts in approximately twenty 20 × 20
pixel regions across the frame. Regions are chosen indi-
vidually by hand to be a representative sample of all areas
of the frame that are more distant than 0.8h−170 Mpc from
the center of the cluster. This is well beyond the radius
at which ICL components have been identified in other
clusters (Krick et al. 2006; Feldmeier et al. 2002; Gonzalez
et al. 2005; Zibetti et al. 2005). The average of these back-
ground regions for each frame is subtracted from the data,
bringing every frame to a zero background. The accuracy
of the background subtraction is discussed in §5.3.
The remaining flux in the cluster images after back-
ground subtraction is corrected for atmospheric extinction
by multiplying each individual image by 10τχ/2.5, where χ
is the airmass and τ is the fitted extinction in magnitudes
from the photometric solution. This multiplicative correc-
tion is between 1.04 and 2.0 for an airmass range of 1.04
to 1.9.
The IRAF tasks geomap and geotran were used to
find and apply x and y shifts and rotations between all
images of a single cluster. The geotran solution is accu-
rate on average to 0.03 pixels (RMS). Details of the final
combined image after pre-processing, background subtrac-
tion, extinction correction, and registration are included in
Table 2.
4.1. Object Detection
Object detection follows the same methods as Paper I.
We use SExtractor to both find all objects in the combined
frames, and to determine their shape parameters. The de-
tection threshold in the V , B, and r images was defined
such that objects have a minimum of 6 contiguous pixels,
each of which are greater than 1.5σ above the background
sky level. We choose these parameters as a compromise be-
tween detecting faint objects in high signal-to-noise regions
and rejecting noise fluctuations in low signal-to-noise re-
gions. This corresponds to minimum surface brightnesses
which range from of 25.2 to 25.8 mag arcsec−2 in B, 25.9
to 26.9 mag arcsec−2 in V , and 24.7 to 26.4 mag arcsec−2
in r (see Table 2). This range in surface brightness is
due to varying cumulative exposure time in the combined
frames. Shape parameters are determined in SExtractor
using only those pixels above the detection threshold.
4.2. Object Removal & Masking
To measure the ICL we remove all detected objects from
the frame by either subtraction of an analytical profile or
masking. Details of this process are described below.
4.2.1. Stars
Scattered light in the telescope and atmosphere pro-
duce an extended point spread function (PSF) for all ob-
jects. To correct for this effect, we determine the extended
PSF using the profiles of a collection of stars from super-
saturated 4th mag stars to unsaturated 14th magnitude
stars. The radial profiles of these stars were fit together
to form one PSF such that the extremely saturated star
was used to create the profile at large radii and the unsat-
urated stars were used for the inner portion of the profile.
This allows us to create an accurate PSF to a radius of 7′,
shown in Figure 1.
The inner region of the PSF is well fit by a Moffat func-
tion. The outer region is well fit by r−2.0 in the r−band
and r−1.6 in the B−band. In the r−band there is a small
additional halo of light at roughly 50 - 100′′(200-400pix)
around stars imaged on the CCD. The newer, higher qual-
ity, anti-reflection coated interference B−band filter does
not show this halo, which implies that the halo is caused by
reflections in the filter. To test the effect of clouds on the
shape of the PSF we create a second deep PSF from stars
in cluster fields taken under non-photometric conditions.
There is a slight shift of flux in the inner 10 arcseconds
of the PSF profile, which will have no impact on our ICL
measurement.
For each individual, non-saturated star, we subtract a
scaled band–specific profile from the frame in addition to
masking the inner 30′′ of the profile (the region which fol-
lows a Moffat profile). For each individual saturated star,
to be as cautious as possible with the PSF wings, we have
subtracted a stellar profile given the USNO magnitude of
that star, and produced a large mask to cover the inner re-
gions and any bleeding. The mask size is chosen to be twice
4 Krick, Bernstein
the radius at which the star goes below 30mag arcsec−2,
and therefore goes well beyond the surface brightness limit
at which we measure the ICL. We can afford to be liberal
with our saturated star masking since most clusters have
very few saturated stars which are not near the center of
the cluster where we need the unmasked area to measure
any possible ICL.
In the specific case of A3880 there are two saturated
stars (9th and 10th r−band magnitude) within two ar-
cminutes of the core region of the cluster. If we used the
same method of conservatively masking (twice the radius
of the 30 mag arcsec−2 aperture), the entire central region
of the image where we expect to find ICL would be lost.
We therefore consider a less extreme method of removing
the stellar profile by iteratively matching the saturated
stars’ profiles with the known PSF shape. We measure
the saturated star profiles on an image which has had ev-
ery object except for those two saturated stars masked, as
described in §4.2.2. We can then scale our measured PSF
to the star’s profile, at radii where there is expected to be
no contamination from the ICL, and the star’s flux is not
saturated. Since the two stars are within an arcminute of
each other, the scaled profiles of the stars are iteratively
subtracted from the masked cluster image until the process
converges on solutions for the scaling of each star. We still
use a mask for the inner region (∼ 75′′) where saturation
and seeing effect the profile shape.
4.2.2. Galaxies
We want to remove all the flux in our images associated
with galaxies. Although some galaxies might follow de-
Vaucouleurs, Sersic, or exponential profiles, those galaxies
which are near the centers of clusters can not be fit with
these or other models either because of the overcrowding
in the center or because their profiles really are different
due to their location in a dense environment. A variety
of strategies for modeling galaxies within the centers of
clusters were explored in Paper 1 and were found to be in-
adequate for these purposes. Since we can not fit and sub-
tract the galaxies to remove their light, we instead mask
all galaxies in our cluster images.
By masking, we remove from our ICL measurements all
pixels above a surface brightness limit which are centered
on a galaxy as detected by SExtractor. For paper I, we
chose to mask inside of 2 - 2.3 times the radius at which
the galaxy light dropped below 26.4 mag arcsec−2 in r,
akin to 2-2.3 times a Holmberg radius (Holmberg 1958).
Holmberg radii are typically used to denote the outermost
radii of the stellar populations in galaxies.
Galaxy profiles will also have the characteristic underly-
ing shape of the PSF, including the extended halo. How-
ever for a 20th magnitude galaxy, the PSF is below 30 mag
arcsec−2by a radius of 10′′.
Each of the clusters has a different native surface bright-
ness detection threshold based on the illumination correc-
tion and background subtraction, and they are all at dif-
ferent redshifts. However we want to mask galaxies at all
redshifts to the same physical surface brightness to allow
for a meaningful comparison between clusters at different
redshifts. To do this we make a correction for (1+z)4 sur-
face brightness dimming and a k correction for each cluster
when calculating mask sizes. The masks sizes change by
an average of 10% and at most 22% from what they would
have been given the native detection threshold. Both the
native and corrected surface brightness detection thresh-
olds are listed in Table 2. To test the effect of mask size on
the ICL profile and total flux, we also create masks which
are 30% larger and 30% smaller in area than the calcu-
lated mask size. The flux within the masked areas for
these galaxies is on average 25% more than the flux iden-
tified by SExtractor as the corrected isophotal magnitude
for each object.
5. results
Here we discuss our methods for measuring both cluster
and ICL properties as well as a discussion of each individ-
ual cluster in our sample.
5.1. Cluster Properties
Cluster redshift, mass, and velocity dispersion are taken
from the literature, where available, as listed in table 1.
Additional properties that can be identified in our data,
particularly those which may correlate with ICL proper-
ties (cluster membership, flux, dynamical state, and global
density), are discussed below and also summarized in Ta-
ble 1.
5.1.1. Cluster Membership & Flux
Cluster membership and galaxy flux are both deter-
mined using a color magnitude diagram (CMD) of either
B− r vs. r (clusters with z < 0.1) or V − r vs. r (clusters
with z > 0.1). We create color magnitude diagrams for
all clusters using corrected isophotal magnitudes as deter-
mined by SExtractor. Membership is then assigned based
on a galaxy’s position in the diagram. If a given galaxy
is within 1σ of the red cluster sequence (RCS) determined
with a biweight fit, then it is considered a member (fits are
shown in Figure 2). All others are considered to be non-
member foreground or background galaxies. This method
selects the red elliptical galaxies as members. The ben-
efits of this method are that membership can easily be
calculated with 2 band photometry. The drawbacks are
that it both does not include some of the bluer true mem-
bers and does include some of the redder non-members.
An alternative method of determining cluster flux with-
out spectroscopy by integrating under a background sub-
tracted luminosity function is discussed in detail in §5.3
of paper I. Due to the large uncertainties involved in both
methods (∼ 30%), the choice of procedure will not greatly
effect the conclusions.
To determine the total flux in galaxies, we sum the flux
of all member galaxies within the same cluster radius. The
image size of our low-redshift clusters restricts that radius
to one quarter of the virial radius of the cluster where virial
radii are taken from the literature or calculated from X–
ray temperatures as described in §A.1-A.10. From tests
with those clusters where we do have some spectroscopic
membership information from the literature (see §A.3 &
§A.6), we expect the uncertainty in flux from using the
CMD for membership to be ∼ 30%.
Fits to the CMDs produce the mean color of the red
ellipticals, the slope of the color versus magnitude relation
(CMR) for each cluster, and the width of that distribution.
ICL 5
Among our 10 clusters, the color of the red sequence is cor-
related with redshift whereas the slopes of the relations are
roughly the same across redshift, consistent with López-
Cruz et al. (2004). The widths of the CMRs vary from
0.1 to 0.4 magnitudes. This is expected if these clusters
are made up of multiple clumps of galaxies all at similar,
but not exactly the same, redshifts. True background and
foreground groups and clusters can also add to the width
of the RCS.
In order to compare fluxes from all clusters, we consider
two correction factors. First, galaxies below the detection
threshold will not be counted in the cluster flux as we have
measured it, and will instead contribute to the ICL flux.
Since each cluster has a different detection threshold based
mainly on the quality of the illumination correction (see
Table 2), we calculate individually for each cluster the flux
contribution from galaxies below the detection threshold.
Without luminosity functions for each cluster, we adopt
the Goto et al. (2002) luminosity function based on 200
Sloan clusters (α′r = −0.85 ± 0.03). The flux from dwarf
galaxies below the detection threshold ( M = −11 in r) is
less than or equal to 0.1% of the flux from sources above
the detection threshold (our assumed value of total flux).
This is an extremely small contribution due to the faint
end slope, and our deep, uniform images with detection
thresholds in all cases more than 7 magnitudes dimmer
than M∗. Our surface brightness detection thresholds are
low enough that we don’t expect to miss galaxies of nor-
mal surface brightness below our detection threshold at
any redshift assuming that all galaxies at all redshifts have
similar central surface brightnesses.
Second, we apply k and evolutionary corrections to ac-
count for the shifting of the bandpasses through which we
are observing, and the evolution of the galaxy spectra due
to the range in redshifts we observe. We use Poggianti
(1997) for both of these corrections as calculated for sim-
ple stellar population of elliptical galaxies in B, V , and
5.1.2. Dynamical Age
Dynamical age is an important cluster characteristic for
this work as dynamical age is tied to the number of past
interactions among the galaxies. We discuss four methods
for estimating cluster dynamical age based on optical and
X–ray imaging. The first two methods are based on cluster
morphology using Bautz Morgan type and an indication of
the presence of a cD galaxy. We use morphology as a proxy
for dynamical age since clusters with single large ellipti-
cal galaxies at their centers (cD) have presumably been
through more mergers and interactions than clusters that
have multiple clumps of galaxies where none have settled
to the center of the potential. Those clusters with more
mergers are dynamically older, therefore clusters with cD
galaxies should be dynamically older. Specifically Bautz
Morgan type is a measure of cluster morphology defined
such that type I clusters have cD galaxies, type III clusters
do not have cD galaxies, and type II clusters may show
cD-like galaxies which are not centrally located. Bautz
Morgan type is not reliable as Abell did not have mem-
bership information. To this we add our own binary in-
dicator of cluster morphology; clusters which have single
galaxy peaks in the centers of their ICL distributions (cD
galaxies) versus clusters which have multiple galaxy peaks
in the centers of their ICL distributions (no cD).
We have more information about the dynamical age of
the cluster beyond just the presence or absence of a cD
galaxy, namely the difference in brightness of the bright-
est cluster galaxy (BCG) relative to the next few bright-
est galaxies in the cluster (the luminosity gap statistic
Milosavljević et al. 2006), which is our third estimate of
dynamical age. Clusters with one bright galaxy that is
much brighter than any of the other cluster galaxies im-
ply an old dynamic age because it takes time to form that
bright galaxy through multiple mergers. Conversely, mul-
tiple evenly bright galaxies imply a cluster that is dynam-
ically young. For our sample we measure the magnitude
differences between the first (M1) and second (M2) bright-
est galaxies that are considered members based on color.
We run the additional test of comparing M2-M1 with M3-
M1, where consistency between these values insures a lack
of foreground or background contamination. Values of M3-
M1 range from 0.24 to 1.1 magnitudes and are listed in
Table 1. This is the most reliable measure of dynamic
age available to us in this dataset. In a sample of 12
galaxy groups from N-body hydrodynamical simulations,
D’Onghia et al. (2005) find a clear, strong correlation be-
tween the luminosity gap statistic and formation time of
the group (spearman rank coefficient of 0.91) such that
δmag increases by 0.69 ± 0.41(1σ) magnitudes for every
one billion years of formation. We assume this simulation
is also an accurate reflection of the evolution of clusters
and therefore that M3-M1 is well correlated with forma-
tion time and therefore dynamical age of the clusters.
The fourth method for measuring dynamical state is
based on the X–ray observations of the clusters. In a sim-
ulation of 9 cluster mergers with mass ratios ranging from
1:1 to 10:1 with a range of orbital properties, Poole et al.
(2006) show that clusters are virialized at or shortly af-
ter they visually appear relaxed through the absence of
structures (clumps, shocks, cavities) or centroid shifts (X–
ray peak vs. center of the X–ray gas distribution). We
then assume that spherically distributed hot gas as evi-
denced by the X–ray morphologies of the clusters free from
those structures and centroid shifts implies relaxed clusters
which are therefore dynamically older clusters that have
already been through significant mergers. With enough
photons, X–ray spectroscopy can trace the metallicity of
different populations to determine progenitor groups or
clusters. X–ray observations are summarized in §A.1 -
§A.10.
5.1.3. Global Density
Current global cluster density is an important cluster
characteristic for this work as density is correlated with
the past interaction rate among galaxies. We would like a
measure of the number of galaxies in each of the clusters
within some well defined radius which encompasses the po-
tentially dynamically active regions of the cluster. Abell
chose to calculate global density as the number of galaxies
with magnitudes between that of the third ranked member,
M3, and M3+2 within 1.5 Mpc of the cluster, statistically
correcting for foreground and background galaxy contami-
nation with galaxy densities outside of 1.5Mpc (Abell et al.
1989). The cluster galaxy densities are then binned into
6 Krick, Bernstein
richness classes with values of zero to three, where rich-
ness three clusters are higher density than richness zero
clusters. Cluster richnesses are listed in Table 1.
In addition to richness class we use a measure of global
density which has not been binned into coarse values and is
not affected by sample completeness. To do this we count
the number of member galaxies inside of 0.8 h−170 Mpc to
the same absolute magnitude limit for all clusters. Mem-
bership is assigned to those galaxies within 1σ of the color
magnitude relation (CMR). The density may be affected
by the width of the CMR if the CMR has been artificially
widened due to foreground and background contamina-
tion. We choose a magnitude limit of Mr = -18.5 which
is deep enough to get many tens of galaxies at all clus-
ters, but is shallow enough that our photometry is still
complete. At the most distant clusters (z=0.31), an Mr =
-18.5 galaxy is a 125σ detection. The numbers of galax-
ies in each cluster that meet these criteria range from 62
- 288, and are in good agreement with the broader Abell
richness determination. These density estimates are listed
in Table 1.
5.2. ICL properties
We detect an ICL component in all ten clusters of our
sample. We describe below our methods for measuring the
surface brightness profile, color, flux, and substructure in
that component.
5.2.1. Surface brightness profile
In eight out of 10 clusters the ICL component is central-
ized enough to fit with a single set of elliptical isophotes.
The exceptions are A0141 and AC118. We use the IRAF
routine ellipse to fit isophotes to the diffuse light which
gives us a surface brightness profile as a function of semi–
major axis. The masked pixels are completely excluded
from the fits. There are 3 free parameters in the isophote
fitting: center, position angle (PA), and ellipticity. We fix
the center and let the PA and ellipticity vary as a func-
tion of radius. Average ICL ellipticities range from 0.3 to
0.7 and vary smoothly if at all within each cluster. The
PA is notably coincident with that of the cD galaxy where
present (discussed in §A.1 - A.10).
We identify the surface brightness profile of the total
cluster light (ie., including resolved galaxies) for compari-
son with the ICL within the same radial extent. To do this,
we make a new “cluster” image by masking non-member
galaxies as determined from the color magnitude relation
(§5.1.1). A surface brightness profile of the cluster light is
then measured from this image using the same elliptical
isophotes as were used in the ICL profile measurement.
Figure 3 shows the surface brightness profiles of all eight
clusters for which we can measure an ICL profile. Individ-
ual ICL profiles in both r− and V− or B−bands are shown
in Figures 4 - 13. Results based on all three versions of
mask size (as discussed in §4.2.2) are shown via shading on
those plots. Note that we are not able to directly measure
the ICL at small radii (<∼ 70kpc) in any of the clusters
because greater than 75% of those pixels are masked. The
uncertainty in the ICL surface brightness is dominated by
the accuracy with which the background level can be iden-
tified, while the error on the mean within each elliptical
isophote is negligible, as discussed in §5.3. Error bars in
Figures 3 and 4 - 13 show the 1σ uncertainty based on
the error budget for each cluster (see representative error
budget in Table 3).
The ICL surface brightness profiles have two interesting
characteristics. First, in all cases they can be fit by both
exponential and deVaucouleurs profiles. Both appear to
perform equally well given the large error bars at low sur-
face brightness. These profiles, in contrast to the galaxy
profiles, are relatively smooth, only occasionally reflect-
ing the clustering of galaxies. Second, the ICL is more
concentrated than the galaxies, which is to say that the
ICL falls off more rapidly with increased radius than the
galaxy light. In all cases the ICL light is decreasing rapidly
enough at large radii such that the additional flux beyond
the radius at which we can reliable measure the surface
brightness is at most 10% of the flux inside of that radius
based on an extrapolation of the exponential fit.
There are 2 clusters (A0141, Figure 11 & AC118, Fig-
ure 13) for which there is no single centralized ICL profile.
These clusters do not have a cD galaxy, and their giant el-
lipticals are distant enough from each other that the ICL is
not a continuous centralized structure. We therefore have
no surface brightness profile for those clusters although we
are still able to measure an ICL flux, as discussed below.
We attempt to measure the profile of the cD galaxy
where present in our sample. To do this we remove the
mask of that galaxy and allow ellipse to fit isophotes all
the way into the center. In 5 out of 7 clusters with a cD
galaxy, the density of galaxies at the center is so great that
just removing the mask for the cD galaxy is not enough
to reveal the center of the cluster due to the other over-
lapping galaxies. Only for A4059 and A2734 are we able
to connect the ICL profile to the cD profile at small radii.
These are shown in Figures 4 & 6.
In both cases the entire profile of the cD plus ICL is well
fit by a single DeVaucouleurs profile, although it can also
be fit by 2 DeVaucouleurs profiles. The profiles can not
be fit with single exponential functions. We do not see a
break between the cD and ICL profiles as seen by Gonzalez
et al. (2005). While those authors find that breaks in the
extended BCG profile are common in their sample, ∼ 25%
of the BCG’s in that sample did not show a clear pref-
erence for a double deVaucouleurs model over the single
deVaucouleurs model. In both clusters where we measure
a cD profile, the color appears to start out with a blue
color gradient, and then turn around and become increas-
ingly redder at large radii as the ICL component becomes
dominant (see Figures 4 & 6).
5.2.2. ICL Flux
The total amount of light in the ICL and the ratio of
ICL flux to total cluster flux can help constrain the impor-
tance of galaxy disruption in the evolution of clusters. As
some clusters have cD galaxies in the centers of their ICL
distribution, we need a consistent, physically motivated
method of measuring ICL flux in the centers of those clus-
ters as compared to the clusters without a single central-
ized galaxy. The key difference here is that in cD clusters
the ICL stars will blend smoothly into the galaxy occupy-
ing the center of the potential well, whereas with non-cD
clusters the ICL stars in the center are unambiguous. Since
our physical motivation is to understand galaxy interac-
ICL 7
tions, we consider ICL to be all stars which were at some
point stripped from their original host galaxies, regardless
of where they are now.
In the case of clusters with cD galaxies, although we
cannot separate the ICL from the galaxy flux in the cen-
ter of the cluster, we can measure the ICL profile outside
of the cD galaxy. Gonzalez et al. (2005) have shown for
a sample of 24 clusters that a BCG with ICL halo can be
well fit with two deVaucouleurs profiles. The two profiles
imply two populations of stars which follow different or-
bits. We assume stars on the inner profile are cD galaxy
stars and those stars on the outer profile are ICL stars.
Gonzalez et al. (2005) find that the outer profile on aver-
age accounts for 80% of the combined flux and becomes
dominant at 40-100kpc from the center which is at sur-
face brightness levels of 24 - 25 mag arcsec−2 in r. Since
all of our profiles are well beyond this radius and well be-
low this surface brightness level, we conclude that the ICL
profile we identify is not contaminated by cD galaxy stars.
Assuming that the stars on the outer profile have differ-
ent orbits than the stars on the inner profile, we calculate
ICL flux by summing all the light in the outer profile from
a radius of zero to the radius at which the ICL becomes
undetectable. Note that this method identifies ICL stars
regardless of their current state as bound or unbound from
the cD galaxy.
We therefore calculate ICL flux by first finding the
mean surface brightness in each elliptical annuli where all
masked pixels are not included. This mean flux is then
summed over all pixels within that annulus including the
ones which were masked. This represents a difference from
paper I where we performed an integration on the fit to the
ICL profile; here we sum the profile values themselves. We
are justified in using the area under the galaxy masks for
the ICL sum since the galaxies only account for less than
3% of the volume of the cluster regardless of projected
area.
There are two non-cD clusters (A141 & A118) for which
we could not recover a profile. We calculate ICL flux for
those clusters by measuring a mean flux within three con-
centric, manually–placed, elliptical annuli (again not uti-
lizing masked pixels) in the mean, and then summing that
flux over all pixels in those annuli. All ICL fluxes are
subject to the same k and evolutionary corrections as dis-
cussed in §5.1.1.
5.2.3. ICL Fraction
In addition to fluxes, we present the ratio of ICL flux
to total cluster flux, where total cluster flux includes ICL
plus galaxy flux. Galaxy flux is taken from the CMDs out
to 0.25rvirial, as discussed in §5.1.1. ICL fractions range
from 6 to 22% in the r−band and 4 to 21% in the B−band
where the smallest fraction comes from A2556 and the
largest from A4059. All fluxes and fractions are listed in
Table 1. As mentioned in §5.1.1, there is no perfect way of
measuring cluster flux without a complete spectroscopic
survey. Based on those clusters where we do have some
spectroscopic information, we estimate the uncertainty in
the cluster flux to be ∼ 30% . This includes both the ab-
sence from the calculation of true member galaxies, and
the false inclusion of non-member galaxies.
All cluster fluxes as measured from the RCS do not in-
clude blue member galaxies so those fluxes are potentially
lower limits to the true cluster flux, implying that the ICL
fractions are potentially biased high. This possible bias
is made more complicated by the known fact that not all
clusters have the same amount of blue member galaxies
(Butcher & Oemler 1984). Less evolved clusters (at higher
redshifts) will have higher fractions of blue galaxies than
more evolved clusters (at lower redshifts). Therefore ICL
fractions in the higher redshift clusters will be systemati-
cally higher than in the lower redshift clusters since their
fluxes will be systematically underestimated. We estimate
the impact of this effect using blue fractions from Couch
et al. (1998) who find maximal blue fractions of 60% of all
cluster galaxies at z = 0.3 as compared to ∼ 20% at the
present epoch. If none of those blue galaxies were included
in our flux measurement for AC114 and AC118 (the two
highest z clusters), this implies a drop in ICL fraction of
∼ 40% as compared to ∼ 10% at the lowest redshifts. This
effect will strengthen the relations discussed below.
Most simulations use a theoretically motivated defini-
tion of ICL which determine its fractional flux within r200
or rvir. It is not straightforward to compare our data to
those simulated values since our images do not extend to
the virial radius nor do they extend to infinitely low surface
brightness which keeps us from measuring both galaxy and
ICL flux at those large radii. The change in fractional flux
from 0.25rvir to rvir will be related to the relative slopes
of the galaxies versus ICL. As the ICL is more centrally
concentrated than the galaxies we expect the fractional
flux to decrease from 0.25rvir to rvir since the galaxies
will contribute an ever larger fraction to the total cluster
flux at large radii. We estimate what the fraction at rvir
would be for 2 clusters in our sample, A4059 and A3984
(steep profile and shallow profile respectively), by extrap-
olating the exponential fits to both the ICL and galaxy
profiles. Using the extrapolated flux values, the fractional
flux decreases by 10% where ICL and galaxy profiles are
steep and up to 90% where profiles are shallower.
5.2.4. Color
For those clusters with an ICL surface brightness pro-
file we measure a color profile as a function of radius by
binning together three to four points from the surface
brightness profile. All colors are k corrected and evolution
corrected assuming a simple stellar population (Poggianti
1997). Color profiles range from flat to increasingly red or
increasingly blue color gradients (see Figures 14). We fit
simple linear functions to the color profiles with their cor-
responding errors. To determine if the color gradients are
statistically significant we look at the ±2σ values on the
slope of the linear fit. If those values do not include zero
slope, then we assume the color gradient is real. Color er-
ror bars are quite large, so in most cases 2σ does include a
flat profile. The significant color gradients (A4010, A3888,
A3984) are discussed in §A.1 - A.10.
For all clusters an average ICL color is used to compare
with cluster properties. In the case where there is a color
gradient, that average color is taken as an average of all
points with error bars less than one magnitude.
5.2.5. ICL Substructure
Using the technique of unsharp masking (subtracting a
smoothed version of the image from itself) we scan each
8 Krick, Bernstein
cluster for low surface brightness (LSB) tidal features as
evidence of ongoing galaxy interactions and thus possible
ongoing contribution to the ICL . All 10 clusters do have
multiple LSB features which are likely from tidal interac-
tions between galaxies, although some are possibly LSB
galaxies seen edge on. For example we see multiple inter-
acting galaxies and warped galaxies, as well as one shell
galaxy. For further discussion see §6.5 of paper I. From the
literature we know that the two highest redshift clusters
in the sample (AC114 and AC118, z=0.31) have a higher
fraction of interacting galaxies than other clusters (∼ 12%
of galaxies, Couch et al. 1998). In two of our clusters,
A3984 and A141, there appears to be plume-like structure
in the diffuse ICL, which is to say that the ICL stretches
from the BCG towards another set of galaxies. Of this
sample, only A3888 has a large, hundred kpc scale, arc
type feature, see Figure 9 and Table 2 of paper I. There
are ∼ 4 examples of these large features in the literature
(Gregg & West 1998; Calcáneo-Roldán et al. 2000; Feld-
meier et al. 2004; Mihos et al. 2005). These structures
are not expected to last longer than a few cluster cross-
ing times, so we don’t expect that they must exist in our
sample. Furthermore, it is possible that there is signifi-
cant ICL substructure below our surface brightness limits
(Rudick et al. 2006).
5.2.6. Groups
In seven out of 10 clusters the diffuse ICL is determined
by eye to be multi-peaked
(A4059,A2734,A3888,A3984,A141,AC114,AC118). In
some cases those excesses surround the clumps of galax-
ies which appear to all be part of the same cluster, ie
the clumps are within a few hundred kpc from the cen-
ter but have obvious separations, and there is no central
dominant galaxy (eg., A118). In other cases, the sec-
ondary diffuse components are at least a Mpc from the
cluster center (eg., A3888). In these cases, the secondary
diffuse light component is likely associated with groups
of galaxies which are falling in toward the center of the
cluster, and may be at various different stages of merg-
ing at the center. This is strong evidence for ICL cre-
ation in group environments, which is consistent with re-
cent measurements of a small amount of ICL in isolated
galaxy groups (Castro-Rodŕıguez et al. 2003; Durrell et al.
2004; Rocha & de Oliveira 2005). This is also consistent
with current simulations (Willman et al. 2004; Fujita 2004;
Gnedin 2003b; Rudick et al. 2006; Sommer-Larsen 2006,
and references therein). From the theory, we expect ICL
formation to be linked with the number density of galax-
ies. Since group environments can have high densities at
their centers and have lower velocity dispersions, it is not
surprising that groups have ICL flux associated with them.
Sommer-Larsen (2006) find the intra-group light to have
very similar properties to the ICL making up 12 − 45%
of the group light, having roughly deVaucouleurs profiles,
and in general varying in flux from group to group where
groups with older dynamic ages (fossil groups D’Onghia
et al. 2005) have a larger amount of ICL. Groups in indi-
vidual clusters are discussed in §A.1 - A.10.
5.3. Accuracy Limits
The accuracy of the ICL surface brightness is limited
on small scales (< 10′′) by photon noise. On larger scales
(> 10′′), structure in the background level (be it intrinsic
or instrumental) will dominate the error budget. We de-
termine the stability of the background level in each clus-
ter image on large scales by first median smoothing the
masked image by 20′′. We then measure the mean flux in
thousands of random 1′′ regions more distant than 0.8 Mpc
from the center of the cluster. The standard deviation of
these regions represents the accuracy with which we can
measure the background on 20′′ scales. We tested the accu-
racy of this measure for even larger-scale uncertainties on
two clusters (A3880 from the 40” data and A3888 from the
100” data). We find that the uncertainty remains roughly
constant on scales equal to, or larger than, 20′′. These
accuracies are listed for each cluster in Table 2. Regions
from all around the frame are used to check that this es-
timate of standard deviation is universal across the image
and not affected by location in the frame. This empiri-
cal measurement of the large-scale fluctuations across the
image is dominated by the instrumental flat-fielding ac-
curacy, but includes contributions from the bias and dark
subtraction, physical variations in the sky level, and the
statistical uncertainties mentioned above.
We examine the effect of including data taken under
non-photometric conditions on the large-scale background
illumination. This noise is fully accounted for in the mea-
surement described above. All B− and V− band data
were taken on photometric nights. Five clusters include
varying fractions of non-photometric r− band data; 47%
of A3880, 12% of A3888, 15% of A3984, 48% of A141, and
14% of A114 are non-photometric. For A3880, the clus-
ter with one of the largest fractions of non-photometric
data, we compare the measured accuracy on the combined
image which includes the non-photometric data with accu-
racy measured from a combined image which includes only
photometric frames. The resulting large-scale accuracy is
0.3 mag arcsec−2better on the frame which includes only
photometric data. Although this does imply that the non-
photometric frames are noisier, the added signal strength
gained from having 4.5 more hours on source outweighs
the extra noise.
This empirical measurement of the large–scale back-
ground fluctuations is likely to be a conservative estimate
of the accuracy with which we can measure surface bright-
ness on large scales because it is derived from the outer
regions of the image where compared to the central re-
gions on average a factor of ∼ 2 fewer individual exposures
have been combined for the 100” data and a factor of 1.5
for the 40” (which has a larger field of view and requires
less dithering). A larger number of dithered exposures at
a range of airmass, lunar phase, photometric conditions,
time of year, time of night, and distance to the moon has
the effect of smoothing out large-scale fluctuations in the
illumination pattern. We therefore expect greater accu-
racy in the center of the image where the ICL is being
measured.
We include a list all sources of uncertainty for one cluster
in our sample (A3888) in Table 3 (reproduced here from
Paper I). In addition to the dominant uncertainty due to
the large-scale fluctuations on the background as discussed
above, we quantify the contributions from the photometry,
masking, and the accuracy with which we can measure the
mean in the individual elliptical isophotes. Errors for the
ICL 9
other clusters are similarly dominated by background fluc-
tuations, which are listed in Table 2. The errors on the
total ICL fluxes in all bands range from 17% to 70% with
an average of 39%. The exception is A2556 which reaches a
flux error of 100% in the B−band due to its extremely faint
profile (see §A.4). Assuming a 30% error in the galaxy flux
(see §5.1.1), the errors on the ICL fraction are on average
48%. The errors plotted on the surface brightness profiles
are the 1σ errors.
6. discussion
We measure a diffuse intracluster component in all ten
clusters in our sample. Clues to the physical mechanisms
driving galaxy evolution come from comparing ICL prop-
erties with cluster properties. We have searched for corre-
lations between the entire set of properties. Pairs of prop-
erties not explicitly discussed below showed no correla-
tions. Limited by a small sample and non-parametric data,
we use a Spearman rank test to determine the strength
of any possible correlations where 1.0 or -1.0 indicate a
definite correlation or anti–correlation respectively, and 0
indicates no correlation. Note that this test does not take
into account the errors in the parameters, and instead only
depends on their rank among the sample. Where a corre-
lation is indicated we show the fit as well as ±2σ in both
y-intercept and slope to graphically show the ranges of the
fit, and give some estimate of the strength of the correla-
tion.
There are selection biases in our data between cluster
parameters due to our use of an Abell selected sample.
The Abell cluster sample is incomplete at high redshifts;
it does not include low-mass, low-luminosity, low-density,
high-redshift clusters because of the difficulty in obtaining
the required sensitivity with increasing redshift. Although
our 5 low-redshift clusters are not affected by this selection
effect, and should be a random sampling, small numbers
prevent those clusters from being fully representative of
the entire range of cluster properties.
Specifically we discuss the possibility that there is a real
trend underlying the selection bias in the cases of lower
luminosity (Figure 15) and lower density clusters (Figure
16) being preferentially found at lower redshift. Clusters
in our sample with less total galaxy flux are preferentially
found at low redshifts, however hierarchical formation pre-
dicts the opposite trend; clusters should be gaining mass
over time and hence light over time. Note that on size
scales much larger than the virial radius mass does not
change with time and therefore those systems can be con-
sidered as closed boxes; but on the size scales of our data,
a quarter of a virial radius, clusters are not closed boxes.
We might expect a slight trend, as was found, such that
lower density clusters are found at lower redshifts. As a
cluster ages, it converts a larger number of galaxies into
a smaller number of galaxies via merging and therefore
has a lower density at lower redshifts despite being more
massive than high redshift clusters. The infall of galaxies
works against this trend. The sum total of merger and
infall rates will control this evolution of density with red-
shift. The observed density redshift relation for this sam-
ple is strong; over the range z=0.3 - 0.05 (elapsed time of
3Gyr assuming standard ΛCDM) the projected number
density of galaxies has to change by a factor of 5.5, imply-
ing that every 5.5 galaxies in the cluster must have merged
into 1 galaxy in the last 3 Gyr. This is well above a re-
alistic merger rate for this timescale and this time period
(Gnedin 2003a). Instead it is likely that we are seeing the
result of a selection effect.
An interesting correlation which may be indirectly due
to the selection bias is that clusters with less total galaxy
flux tend to have lower densities (Figure 17). While we ex-
pect a smaller number of average galaxies to emit a smaller
amount of total light, it is possible that the low density
clusters are actually made up of a few very bright galaxies.
So although the trend might be real, it is also likely that
the redshift selection effect of both density and cluster flux
is causing these two parameters to be correlated.
A correlation which does not appear to be affected by
sample selection is that lower density clusters in our sam-
ple are weakly correlated with the presence of a cD galaxy,
see Figure 18. A possible explanation for this is that as
a cluster ages it will have made a cD galaxy out of many
smaller galaxies, so the density will actually be lower for
dynamically older clusters. Loh & Strauss (2006) find the
same correlation by looking at a sample of environments
around 2000 SDSS luminous red galaxies.
In the remainder of this section we examine the inter-
esting physics that can be gleaned from the combination
of cluster properties and ICL properties given the above
biases. The interpretation of ICL correlations with clus-
ter properties is highly complicated due not only to small
number statistics and the selection bias, but to the direc-
tion of the selection bias. Biases in mass, density, and
total galaxy flux with redshift will destructively combine
to cancel the trends which we expect to find in the ICL (as
described in the introduction). An added level of compli-
cation is due to the fact that we expect the ICL flux to be
evolving with time. We examine below each ICL property
in turn, including how the selection bias will effect any
conclusions drawn from the observed trends.
6.1. ICL flux
We see a range in ICL flux likely caused by the differ-
ing interaction rates and therefore differing production of
tidal tails, streams, plumes, etc. in different clusters. Clus-
ters include a large amount of tidal features at low surface
brightness as evidenced by their discovery at low redshift
where they are not as affected by surface brightness dim-
ming (Mihos et al. 2005). It is therefore not surprising
that we see a variation of flux levels in our own sample.
ICL flux is apparently correlated with three cluster pa-
rameters; M3-M1, density, and total galaxy flux (Figures
19, 17, & 20). There is no direct, significant correlation
between ICL flux and redshift. As discussed above, the se-
lection effects of density and mass with redshift will tend
to cancel any expected trends in either density, mass, or
redshift. We therefore are unable to draw conclusions from
these correlations. Zibetti et al. (2005), who have a sam-
ple of 680 SDSS clusters, are able to split their sample on
both richness and magnitude of the BCG (as a proxy for
mass). They find that both richer clusters and brighter
BCG clusters have brighter ICL than poor or faint clus-
ters.
6.1.1. ICL Flux vs. M3-M1
10 Krick, Bernstein
Figure 19 shows the moderate correlation between ICL
flux and M3-M1 such that clusters with cD galaxies have
less ICL than clusters without cD galaxies (Spearman coef-
ficient of -0.50). Although we choose M3-M1 to be cautious
about interlopers, M2-M1 shows the same trend with a
slightly more significant spearman coefficient of -0.61. Our
simple binary indicator of the presence of a cD galaxy gives
the same result. Clusters with cD galaxies (7) have an av-
erage flux of 2.3±0.96×1011(1σ) whereas clusters without
cD galaxies (3) have an average flux of 5.0±0.18×1011(1σ).
Although density is correlated with M3-M1, and density
is affected by incompleteness, this trend of ICL flux with
M3-M1 is not necessarily caused by that selection effect.
Furthermore, the correlation of M3-M1 with redshift is
much weaker (if there at all) than trends of either density
or cluster flux with redshift. If the observed relation is due
to the selection effect then we are prevented from drawing
conclusions from this relation. Otherwise, if this relation
between ICL flux and the presence of a cD galaxy is not
caused by a selection effect, then we conclude that the
lower levels of measured ICL are a result of the ICL stars
being indistinguishable form the cD galaxy and therefore
the ICL is evolving in a similar way to a cD galaxy.
By which physical mechanism can the ICL stars end
up in the center of the cluster and therefore overlap with
cD stars? cD galaxies indicate multiple major mergers
of galaxies which have lost enough energy or angular mo-
mentum to now reside in the center of the cluster potential
well. ICL stars on their own will not be able to migrate to
the center over any physically reasonable timescales unless
they were stripped at the center, or are formed in groups
and get pulled into the center along with their original
groups(Merritt 1984).
Assuming the ICL is observationally inseparable from
the cD galaxy, we investigate how much ICL light the mea-
sured relation implies is hidden amongst the stars of the cD
galaxy. If 20% of the total cD + ICL light is added to the
value of the ICL flux in the outer profile, then the observed
trend of ICL flux with M3-M1 is weakened (Spearman co-
efficient drops from 0.5 to 0.4). If 30% of the total cD +
ICL light is hidden in the inner profile then the relation
disappears (Spearman coefficient of 0.22). The measured
relation between ICL r−band flux and dynamical age of
the clusters may then imply that 25-40% of the ICL is coin-
cident with the cD galaxy in dynamically relaxed clusters.
6.2. ICL fraction
We focus now on the fraction of total cluster light which
is in the diffuse ICL. If ICL and galaxy flux do scale to-
gether (not just due to the selection effect), then the ICL
fraction is the physically meaningful parameter in compar-
ison to cluster properties.
ICL fraction is apparently correlated with both mass
and redshift (Figure 21 & 22) and not with density or total
galaxy flux. The selection effect will again work against
the predicted trend of ICL fraction to increase with in-
creasing mass (Murante et al. 2004; Lin & Mohr 2004)
and increasing density. Therefore the lack of trends of
ICL fraction with mass and density could be attributable
to the selection bias.
6.2.1. ICL fraction vs. Mass
We find no trend in ICL fraction with mass. Our data
for ICL fraction as a function of mass is inconsistent with
the theoretical predictions of Murante et al. (2004), Mu-
rante et al. (2007) (based on a cosmological hydrodynam-
ical simulation including radiative cooling, star formation,
and supernova feedback), and Lin & Mohr (2004)(based on
a model of cluster mass and the luminosity of the BCG).
However Murante et al. (2007) show a large scatter of ICL
fractions within each mass bin. They also discuss the
dependence of a simulations mass resolution on the ICL
fraction. These theoretical predictions are over-plotted on
Figure 21. Note that the simulations generally report the
fractional light in the ICL out to much larger radii (rvirial
or r200) than its surface brightness can be measured ob-
servationally. To compare the theoretical predictions at
rvirial to our measurement at 0.25rvirial, the predicted
values should be raised by some significant amount which
depends on the ICL and galaxy light profiles at large radii.
This makes the predictions and the data even more incon-
sistent than it first appears. As an example of the differ-
ences, a cluster with the measured ICL fraction of A3888
would require a factor of greater than 100 lower mass than
the literature values to fall along the predicted trend. Al-
though these clusters are not dynamically relaxed, such
large errors in mass are not expected. As an upper limit
on the ICL flux, if we assumed the entire cD galaxy was
made of intracluster stars, that flux plus the measured ICL
flux would still not be enough to raise the ICL fractions
to the levels predicted by these authors.
There are no evident correlations between velocity dis-
persion and ICL characteristics, although velocity disper-
sion is a mass estimator. Large uncertainties are presum-
ably responsible for the lack of correlation.
6.2.2. ICL fraction vs. Redshift
Figure 22 is a plot of redshift versus ICL fraction for
both the r− andB−or V−bands. We find a marginal anti–
correlation between ICL fraction and redshift with a very
shallow slope, if at all, in the direction that low redshift
clusters have higher ICL fractions (Spearman rank coeffi-
cient of -0.43). This relation is strengthened when assum-
ing fractions of blue galaxies are higher in the higher red-
shift clusters(spearman rank of -0.6) (see §5.2.3). A trend
of ICL fraction with redshift tells us about the timescales
of the mechanisms involved in stripping stars from galax-
ies. This relation is possibly affected by the same redshift
selection effects as discussed above.
Over the redshift range of our clusters, 0.31 > z > 0.05,
a chi–squared fit to our data gives a range of fractional
flux of 11 to 14%. Willman et al. (2004) find the ICL
fraction grows from 14 to 19%. over that same redshift
range. Willman et al. (2004) measure the ICL fraction at
r200 which means these values would need to be increased
in order to directly compare with our values. While their
normalization of the relation is not consistent with our
data, the slopes are roughly consistent, with the caveat of
the selection effect. The discrepancy is likely, at least in
part, caused by different definitions of ICL. Simulations
tag those particles which become unbound from galax-
ies whereas in practice we do not have that information
and instead use surface brightness cutoffs and ICL profile
shapes. Rudick et al. (2006) do use a surface brightness
ICL 11
cutoff in their simulations to tag ICL stars which is very
similar to our measurement. They find on average from
their 3 simulated clusters a change of ICL fraction of ap-
proximately 2% over this redshift range. We are not able
to observationally measure such a small change in fraction.
Rudick et al. (2006) predict that in order to grow the ICL
fraction by 10%, on average, we would need to track clus-
ters as they evolve from a redshift of 2 to the present.
However, both Willman et al. (2004) and Rudick et al.
(2006) find that the ICL fraction makes small changes over
short timescales (as major mergers or collisions occur).
6.3. ICL color
The average color of the ICL, is roughly the same as
the color of the red ellipticals in each of the clusters. In
§8.1 of paper I we discuss the implications of this on ICL
formation redshift and metallicity. Zibetti et al. (2005)
have summed g−, r−, and i− band imaging of 680 clus-
ters in a redshift range of 0.2 - 0.3. Similar to our results,
they find that the summed ICL component has roughly
the same g − r color at all radii as the summed cluster
population including the galaxies. Since we have applied
an evolutionary correction to the ICL colors, if there is
only passive color evolution, the ICL will show no trend
with redshift. Indeed we find no correlation between B−r
color and the redshift of the cluster, as shown in Figure 23
(B − r = 2.3 ± 0.2(1σ)). ICL color may have the ability
to broadly constrain the epoch at which these stars were
stripped. In principle, as mentioned in the introduction,
we could learn at which epoch the ICL had been stripped
from the galaxies based on its color relative to the galaxies
assuming passively evolving ICL and ongoing star forma-
tion in galaxies. While this simple theory should be true,
the color difference between passively evolving stars and
low star forming galaxies may not be large enough to de-
tect since clusters are not made up of galaxies which were
all formed at a single epoch and we don’t know the star
formation rates of galaxies once they enter a cluster.
ICL color may have the ability to determine the types
of galaxies from which the stars are being stripped. Un-
fortunately the difference in color between stars stripped
from ellipticals, and for example stars stripped from low
surface brightness dwarfs is not large enough to confirm in
our data given the large amount of scatter in the color of
the ICL (see paper I for a more complete discussion).
There is no correlation in our sample between the pres-
ence or direction of ICL color gradients and any cluster
properties. This is very curious since we see both blue-
ward and red-ward color gradients. A larger sample with
more accurate colors and without a selection bias might
be able to determine the origin of the color gradients.
6.4. Profile Shape
Figure 3 shows all eight surface brightness profiles for
clusters that have central ICL components. To facilitate
comparison, we have shifted all surface brightnesses to a
redshift of zero, including a correction for surface bright-
ness dimming, a k–correction, and an evolution correction.
We see a range in ICL profile shape from cluster to cluster.
This is consistent with the range of scale-lengths found in
other surveys (Gonzalez et al. 2005, find a range of scale
lengths from 18 - 480 kpc, fairly evenly distributed be-
tween 30 and 250 kpc) .
The profiles are equally well fit with the empirically
motivated deVaucouleurs profiles and simple exponential
profiles which are shown in the individual profile plots in
Figures 4 - 13. The profiles can also be fit with a Hubble–
Reynolds profile which is a good substitute for the more
complicated surface brightness profile of an NFW density
profile ( Lokas & Mamon 2001). An example of this profile
shape is shown in Figure 3 with a 100 kpc scale length de-
fined as the radius inside of which the profile contains 25%
of the luminosity. This profile shape is what you would
predict given a simple spherical collapse model. The phys-
ically motivated Hubble–Reynolds profile gives acceptable
fits to the ICL profiles with the exception of A4059, A2734,
& A2556 which have steeper profiles. We explore causes
of the differing profile shapes for these three clusters.
A steeper profile is correlated with M3-M1, density, to-
tal cluster flux, and redshift. These three clusters have an
average M3-M1 value of 0.93 ± 0.27 as compared to the
average of 0.49± 0.20 for the remaining 7 clusters. These
three clusters are also three of the four lowest redshift clus-
ters, have an average of 93 galaxies which is 45% smaller
than the value for the remaining sample, and have an av-
erage cluster flux of 12.3 × 1011L�which is 47% smaller
than the value for the remaining sample.
We have the same difficulties here in distinguishing be-
tween the selection effects and the true physical correla-
tions. The key difference is that the three clusters with the
steepest profiles are the most relaxed clusters (which is not
a redshift selection effect). We use “most relaxed” to de-
scribe the three clusters with the most symmetric X–ray
isophotes that have single, central, smooth ICL profiles.
This is consistent with our finding that M3-M1 is a key in-
dicator of ICL flux in §6.1.1 and that ICL can form either
in groups at early times or at later times through galaxy in-
teractions in the dense part of the cluster. If galaxy groups
in which the ICL formed are able to get to the cluster cen-
ter then their ICL will also be found in the cluster center,
and can be hiding in the cD galaxy. If the galaxy groups
in which the ICL formed have not coalesced in the center
then the ICL will be less centrally distributed and there-
fore have a shallower profile. This is consistent with the
recent numerical work by Murante et al. (2007) who find
that the majority of the ICL is formed by the merging
processes which create the BCG’s in clusters. This pro-
cess leads o the ICL having a steeper profile shape than
the galaxies and having greater than half of the ICL be
located inside of 250h−170 kpc, approaching radii where we
do not measure the ICL due to the presence of the BCG.
Their simulations also confirm that different clusters with
different dynamical histories will have differing amounts
and locations of ICL.
7. conclusion
We have identified an intracluster light component in all
10 clusters which has fluxes ranging from 0.76 × 1011 to
7.0×1011 h−170 L�in r and 0.14×10
11 to 1.2×1011 h−170 L�in
the B−band, ICL fractions of 6 to 22% of the total cluster
light within one quarter of the virial radius in r and 4 to
21% in the B−band, and B−r colors ranging from 1.49 to
2.75 magnitudes. This work shows that there is detectable
12 Krick, Bernstein
ICL in clusters and groups out to redshifts of at least 0.3,
and in two bands including the shorter wavelength B− or
V−band.
The interpretation of our results is complicated by small
number statistics, redshift selection effects of Abell clus-
ters, and the fact that the ICL is evolving with time. Of
the cluster properties (M3-M1, density, redshift, and clus-
ter flux), only M3-M1 and redshift are not correlated. As
a result of these selection effects ICL flux is apparently
correlated with density and total galaxy flux but not with
redshift or mass and ICL fraction is apparently correlated
with redshift but not with M3-M1, density, total galaxy
flux, or mass. However, we do draw conclusions from the
ICL color, average values of the ICL fractions, the relation
between ICL flux and M3-M1, and the ICL profile shape.
We find a passively evolving ICL color which is similar
to the color of the RCS at the redshift of each cluster.
The relations between ICL fraction with redshift and ICL
fraction with mass show the disagreement of our data with
simulations since our fractional fluxes are lower than those
predictions. These discrepancies do not seem to be caused
by the details of our measurement.
Furthermore we find evidence that clusters with sym-
metric X–ray profiles and cD galaxies have both less ICL
flux and significantly steeper profiles. The lower amount
of flux can be explained if ICL stars have become in-
distinguishable from cD stars. As the cluster formed a
cD galaxy any groups which participated in the merging
brought their ICL stars with them, as well as created more
ICL through interactions. If a cD does not form, then the
ICL already in groups or actively forming is also prevented
from becoming very centralized as it has no way of loos-
ing energy or angular momentum on its own. While the
galaxies or groups are subject to tidal forces and dynam-
ical friction, the ICL, once stripped, will not be able to
loose energy and/or angular momentum to these forces,
and instead will stay on the orbit on which it formed.
Observed density may not be a good predictor of ICL
properties since it does not directly indicate the density
at the time in which the ICL was formed. We do indeed
expect density at any one epoch to be linked to ICL pro-
duction at that epoch through the interaction rates.
The picture that is emerging from this work is that ICL
is ubiquitous, not only in cD clusters, but in all clus-
ters, and in group environments. The amount of light
in the ICL is dependent upon cluster morphology. ICL
forms from ongoing processes including galaxy–galaxy in-
teractions and tidal interactions with the cluster potential
(Moore et al. 1996; Gnedin 2003b) as well as in groups
(Rudick et al. 2006). With time, as multiple interactions
and dissipation of angular momentum and energy lead
groups already containing ICL to the center of the cluster,
the ICL moves with the galaxies to the center and be-
comes indistinguishable from the cD’s stellar population.
Any ICL forming from galaxy interactions stays on the
orbit where it was formed.
A large, complete sample of clusters, including a pro-
portionate amount with high redshift and low density, will
be able to break the degeneracies present in this work.
Shifting to a lower redshift range will not be as benefi-
cial because a shorter range than presented here will not
be large enough to see the predicted evolution in the ICL
fraction.
In addition to large numbers of clusters it would be
beneficial to go to extremely low surface brightness lev-
els (<∼ 30 mag arcsec−2) to reduce significantly the error
bars on the color measurement and thereby learn about the
progenitor galaxies of the ICL and the timescales for strip-
ping. It will not be easy to achieve these surface brightness
limits for a large sample which includes high-redshift low-
density clusters since those clusters will have very dim ICL
due to both an expected lower amount as correlated with
density, and due to surface brightness dimming.
We acknowledge J. Dalcanton and V. Desai for observ-
ing support and R. Dupke, E. De Filippis, and J. Kemp-
ner for help with X–ray data. We thank the anonymous
referee for useful suggestions on the manuscript. Par-
tial support for J.E.K. was provided by the National Sci-
ence Foundation (NSF) through UM’s NSF ADVANCE
program. Partial support for R.A.B. was provided by a
NASA Hubble Fellowship grant HF-01088.01-97A awarded
by Space Telescope Science Institute, which is operated
by the Association of Universities for Research in As-
tronomy, Inc., for NASA under contract NAS 5-2655.
This research has made use of data from the following
sources: USNOFS Image and Catalogue Archive operated
by the United States Naval Observatory, Flagstaff Station
(http://www.nofs.navy.mil/data/fchpix/); NASA/IPAC
Extragalactic Database (NED), which is operated by the
Jet Propulsion Laboratory, California Institute of Tech-
nology, under contract with the National Aeronautics
and Space Administration; the Two Micron All Sky Sur-
vey, which is a joint project of the University of Mas-
sachusetts and the Infrared Processing and Analysis Cen-
ter/California Institute of Technology, funded by the Na-
tional Aeronautics and Space Administration and the Na-
tional Science Foundation; the SIMBAD database, oper-
ated at CDS, Strasbourg, France; and the High Energy
Astrophysics Science Archive Research Center Online Ser-
vice, provided by the NASA/Goddard Space Flight Cen-
REFERENCES
Abadi, M. G., Moore, B., & Bower, R. G. 1999, MNRAS, 308, 947
Abell, G. O., Corwin, H. G., & Olowin, R. P. 1989, ApJS, 70, 1
Allen, S. W. 1998, MNRAS, 296, 392
Andreon, S., Punzi, G., & Grado, A. 2005, MNRAS, 360, 727
Batuski, D. J., Miller, C. J., Slinglend, K. A., Balkowski, C.,
Maurogordato, S., Cayatte, V., Felenbok, P., & Olowin, R. 1999,
ApJ, 520, 491
Busarello, G., Merluzzi, P., La Barbera, F., Massarotti, M., &
Capaccioli, M. 2002, A&A, 389, 787
Butcher, H. & Oemler, A. 1984, ApJ, 285, 426
Calcáneo-Roldán, C., Moore, B., Bland-Hawthorn, J., Malin, D., &
Sadler, E. M. 2000, MNRAS, 314, 324
Campusano, L. E., Pelló, R., Kneib, J.-P., Le Borgne, J.-F., Fort, B.,
Ellis, R., Mellier, Y., & Smail, I. 2001, A&A, 378, 394
Caretta, C. A., Maia, M. A. G., & Willmer, C. N. A. 2004, AJ, 128,
Castro-Rodŕıguez, N., Aguerri, J. A. L., Arnaboldi, M., Gerhard, O.,
Freeman, K. C., Napolitano, N. R., & Capaccioli, M. 2003, A&A,
405, 803
Choi, Y.-Y., Reynolds, C. S., Heinz, S., Rosenberg, J. L., Perlman,
E. S., & Yang, J. 2004, ApJ, 606, 185
http://www.nofs.navy.mil/data/fchpix/
ICL 13
Colless, M., Dalton, G., Maddox, S., Sutherland, W., Norberg, P.,
Cole, S., Bland-Hawthorn, J., Bridges, T., Cannon, R., Collins,
C., Couch, W., Cross, N., Deeley, K., De Propris, R., Driver, S. P.,
Efstathiou, G., Ellis, R. S., Frenk, C. S., Glazebrook, K., Jackson,
C., Lahav, O., Lewis, I., Lumsden, S., Madgwick, D., Peacock,
J. A., Peterson, B. A., Price, I., Seaborne, M., & Taylor, K. 2001,
MNRAS, 328, 1039
Collins, C. A., Guzzo, L., Nichol, R. C., & Lumsden, S. L. 1995,
MNRAS, 274, 1071
Couch, W. J., Balogh, M. L., Bower, R. G., Smail, I., Glazebrook,
K., & Taylor, M. 2001, ApJ, 549, 820
Couch, W. J., Barger, A. J., Smail, I., Ellis, R. S., & Sharples, R. M.
1998, ApJ, 497, 188
Couch, W. J. & Sharples, R. M. 1987, MNRAS, 229, 423
Cypriano, E. S., Sodré, L. J., Kneib, J.-P., & Campusano, L. E. 2004,
ApJ, 613, 95
Dahle, H., Kaiser, N., Irgens, R. J., Lilje, P. B., & Maddox, S. J.
2002, ApJS, 139, 313
De Filippis, E., Bautz, M. W., Sereno, M., & Garmire, G. P. 2004,
ApJ, 611, 164
D’Onghia, E., Sommer-Larsen, J., Romeo, A. D., Burkert, A.,
Pedersen, K., Portinari, L., & Rasmussen, J. 2005, ApJ, 630, L109
Driver, S. P., Couch, W. J., & Phillipps, S. 1998, MNRAS, 301, 369
Dubinski, J. 1998, ApJ, 502, 141
Durrell, P. R., Decesar, M. E., Ciardullo, R., Hurley-Keller, D., &
Feldmeier, J. J. 2004, in IAU Symposium, ed. P.-A. Duc, J. Braine,
& E. Brinks, 90
Ebeling, H., Voges, W., Bohringer, H., Edge, A. C., Huchra, J. P., &
Briel, U. G. 1996, MNRAS, 281, 799
Feldmeier, J. J., Mihos, J. C., Morrison, H. L., Harding, P., Kaib,
N., & Dubinski, J. 2004, ApJ, 609, 617
Feldmeier, J. J., Mihos, J. C., Morrison, H. L., Rodney, S. A., &
Harding, P. 2002, ApJ, 575, 779
Fujita, Y. 2004, PASJ, 56, 29
Girardi, M., Borgani, S., Giuricin, G., Mardirossian, F., & Mezzetti,
M. 1998a, ApJ, 506, 45
Girardi, M., Giuricin, G., Mardirossian, F., Mezzetti, M., & Boschin,
W. 1998b, ApJ, 505, 74
Girardi, M. & Mezzetti, M. 2001, ApJ, 548, 79
Gnedin, O. Y. 2003a, ApJ, 589, 752
—. 2003b, ApJ, 582, 141
Gonzalez, A. H., Zabludoff, A. I., & Zaritsky, D. 2005, ApJ, 618, 195
Goto, T., Okamura, S., McKay, T. A., Bahcall, N. A., Annis, J.,
Bernard, M., Brinkmann, J., Gómez, P. L., Hansen, S., Kim,
R. S. J., Sekiguchi, M., & Sheth, R. K. 2002, PASJ, 54, 515
Govoni, F., Enßlin, T. A., Feretti, L., & Giovannini, G. 2001, A&A,
369, 441
Gregg, M. D. & West, M. J. 1998, Nature, 396, 549
Holmberg, E. 1958, Meddelanden fran Lunds Astronomiska
Observatorium Serie II, 136, 1
Katgert, P., Mazure, A., den Hartog, R., Adami, C., Biviano, A., &
Perea, J. 1998, A&AS, 129, 399
Krick, J. E., Bernstein, R. A., & Pimbblet, K. A. 2006, AJ, 131, 168
Lin, Y. & Mohr, J. J. 2004, ApJ, 617, 879
Loh, Y.-S. & Strauss, M. A. 2006, MNRAS, 366, 373
 Lokas, E. L. & Mamon, G. A. 2001, MNRAS, 321, 155
López-Cruz, O., Barkhouse, W. A., & Yee, H. K. C. 2004, ApJ, 614,
Merritt, D. 1984, ApJ, 276, 26
Mihos, J. C., Harding, P., Feldmeier, J., & Morrison, H. 2005, ApJ,
631, L41
Milosavljević, M., Miller, C. J., Furlanetto, S. R., & Cooray, A. 2006,
ApJ, 637, L9
Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996,
Nature, 379, 613
Murante, G., Arnaboldi, M., Gerhard, O., Borgani, S., Cheng, L. M.,
Diaferio, A., Dolag, K., Moscardini, L., Tormen, G., Tornatore, L.,
& Tozzi, P. 2004, ApJ, 607, L83
Murante, G., Giovalli, M., Gerhard, O., Arnaboldi, M., Borgani, S.,
& Dolag, K. 2007, ArXiv Astrophysics e-prints
Muriel, H., Quintana, H., Infante, L., Lambas, D. G., & Way, M. J.
2002, AJ, 124, 1934
Pimbblet, K. A., Smail, I., Kodama, T., Couch, W. J., Edge, A. C.,
Zabludoff, A. I., & O’Hely, E. 2002, MNRAS, 331, 333
Poggianti, B. M. 1997, A&AS, 122, 399
Poole, G. B., Fardal, M. A., Babul, A., McCarthy, I. G., Quinn, T.,
& Wadsley, J. 2006, MNRAS, 373, 881
Reimers, D., Koehler, T., & Wisotzki, L. 1996, A&AS, 115, 235
Reiprich, T. H. & Böhringer, H. 2002, ApJ, 567, 716
Rocha, C. D. & de Oliveira, C. M. 2005, MNRAS, 364, 1069
Rudick, C. S., Mihos, J. C., & McBride, C. 2006, ApJ, 648, 936
Smith, R. E., Dahle, H., Maddox, S. J., & Lilje, P. B. 2004, ApJ,
617, 811
Sommer-Larsen, J. 2006, MNRAS, 369, 958
Struble, M. F. & Rood, H. J. 1999, ApJS, 125, 35
Teague, P. F., Carter, D., & Gray, P. M. 1990, ApJS, 72, 715
Willman, B., Governato, F., Wadsley, J., & Quinn, T. 2004, MNRAS,
355, 159
Wu, X., Xue, Y., & Fang, L. 1999, ApJ, 524, 22
Zibetti, S., White, S. D. M., Schneider, D. P., & Brinkmann, J. 2005,
MNRAS, 358, 949
14 Krick, Bernstein
ICL 15
16 Krick, Bernstein
Table 3
Error Budget
Source contribution to ICL uncertainty (%)
1σ uncertainty µ(0′′- 100′′) µ(100′′- 200′′) total ICL flux
(V ) (r) (V ) (r) (V ) (r) (V ) (r)
background levela 29.5 mag arcsec−2 28.8 mag arcsec−2 14 18 39 45 24 31
photometry 0.02 mag 0.03 mag 2 3 2 3 2 3
maskingb variation in mask area ±30 5 5 14 19 9 12
std. dev. in meanc 32.7 mag arcsec−2 32.7 mag arcsec−2 3 2 2 1 3 1
(total) 15 19 41 50 26 33
cluster fluxd 16% 16% · · · · · · · · · · · · · · · · · ·
Note. — a: Large scale fluctuations in background level are measured empirically and include instrumental
calibration uncertainties as well as and true variations in background level (see §5.3). b: Object masks were scaled
by ±30% in area to test the impact on ICL measurement (see §4.2.2). c: The statistical uncertainty in the mean
surface brightness of the ICL in each isophote. d: Errors on the total cluster flux are based on errors in the fit to the
luminosity function (see §5.1.1).
Fig. 1.— The PSF of the 40-inch Swope telescope at Las Campanas Observatory. The y-axis shows surface brightness scaled to correspond
to the total flux of a zero magnitude star. The profile within 5′′ was measured from unsaturated stars and can be affected by seeing. The
outer profile was measured from two stars with super-saturated cores imaged in two different bands. The profile with the bump in it at 100′′
is the r−band profile, that without the bump is the B−band PSF. The bump in the profile at 100′′ is due to a reflection off the CCD which
then bounces off of the filter, and back down onto the CCD. The outer surface brightness profile decreases as r−2 in the r−band and r−1.6
in the B, shown by the dashed lines. An r−3.9 profile is plotted to show the range in slopes.
ICL 17
Fig. 2.— The color magnitude diagrams for all ten clusters in increasing redshift order from let to right, top to bottom; A4059, A3880, A2734,
A2556, A4010, A3888, A3984, A014, AC114, AC118. All galaxies detected in our image are denoted with a gray star. Those galaxies which
have membership information in the literature are over–plotted with open black triangles (members) or squares (non–members)(membership
references are given in §A.1- A.10). Solid lines indicate a biweight fit to the red sequence with 1σ uncertainties.
18 Krick, Bernstein
Fig. 3.— Surface brightness profiles for the eight clusters with a measurable profile. Profiles are listed on the plot in order of ascending
redshift. To avoid crowding, error bars are only plotted on one of the profiles. Errors on the other profiles are similar at similar surface
brightnesses. All surface brightnesses have been shifted to z = 0 using surface brightness dimming, k, and evolutionary corrections. The
x-axis remains in arcseconds and not in Mpc since the y-axis is in reference to arcseconds. Physical scales are noted on the individual plots
(4 - 13). In addition marks have been placed on each profile at the distances corresponding to 200kpc and 300kpc. Also included as the solid
black line near the bottom of the plot is a Hubble Reynolds surface brightness profile as a proxy for an NFW density profile with a scale
length of 100kpc. The ICL does not have a single uniform amount of flux or profile shape. Profile shape does correlate with dynamical age
where those clusters with steeper profiles are dynamically more relaxed (see §6.4).
ICL 19
Fig. 4.— A4059. The plots moving left to right and top to bottom are as follows. The first is our final combined r−band image zoomed
in on the central cluster region. The second plot shows X–ray isophotes where available. Some clusters were observed during the ROSAT
all sky survey, and so have X–ray luminosities, but have not had targeted observations to allow isophote fitting. Isophote levels are derived
from quick-look images taken from HEASARC. X-ray luminosities of these clusters are listed in Table 1 of paper I and are discussed in the
appendix. The third plot shows our background subtracted, fully masked r−band image of the central region of the cluster, smoothed to aid
in visual identification of the surface brightness levels. Masks are shown in their intermediate levels which are listed in column 7 of Table
2. The six gray-scale levels show surface brightness levels of up to 28.5, 27.7,27.2,26.7 mag arcsec−2. The fourth plot shows the surface
brightness profiles of the ICL (surrounded by shading;r−band on top, V− or B−band on the bottom) and cluster galaxies as a function
of semi-major axis. The bottom axis is in arcseconds and the top axis corresponds to physical scale in Mpc. Error bars represent the 1σ
background identification errors as discussed in §5.3. DeVaucouleurs fits to the entire cD plus ICL profile are over-plotted.
20 Krick, Bernstein
Fig. 5.— A3880, same as Figure 4
ICL 21
Fig. 6.— A2734, same as Figure 4
22 Krick, Bernstein
Fig. 7.— A2556, same as Figure 4
ICL 23
Fig. 8.— A4010, same as Figure 4
24 Krick, Bernstein
Fig. 9.— A3888, same as Figure 4, except here we show the elliptical isophotes of the ICL over-plotted on the surface brightness image.
ICL 25
Fig. 10.— A3984, same as Figure 4
26 Krick, Bernstein
Fig. 11.— A141, same as Figure 4, except we are not able to measure a surface brightness profile or consequently a color profile.
ICL 27
Fig. 12.— A114, same as Figure 4
28 Krick, Bernstein
Fig. 13.— A118, same as Figure 4, except we are not able to measure a surface brightness profile or consequently a color profile.
ICL 29
Fig. 14.— The color profile of the eight clusters where measurement was possible plotted as a function of semi-major axis in arcseconds on
the bottom and Mpc on the top. The average color of the red cluster sequence is shown for comparison, as well as the best fit linear function
to the data.
30 Krick, Bernstein
Fig. 15.— Redshift versus total galaxy flux within one quarter of a virial radius. The Spearman rank coefficient is printed in the upper
right corner. The best fit linear function as well as the lines representing ±2σ in both slope and y intercept are also plotted. The strong
correlation between redshift and total galaxy flux shows the incompleteness of the Abell sample which does not include high-redshift, low-flux
clusters
ICL 31
Fig. 16.— Projected number of galaxies versus redshift. Galaxies brighter than Mr = −18.5 within 800 h−170 kpc are included in this
count, which is used as a proxy for density. The Spearman rank coefficient is printed in the upper left corner. There is a strong correlation
between density and redshift. The best fit linear function is included. While we do expect clusters to become less dense over time, this strong
correlation is not expected. Instead this is due to an incompleteness at high redshift. See §6 for a discussion of the effects of this selection
effect.
32 Krick, Bernstein
Fig. 17.— Projected number of galaxies versus ICL luminosity. ICL luminosity shows 1σ error bars and has been K and evolution corrected.
Galaxies brighter than Mr = −18.5 within 800 h−170 kpc are included in this count, which is used as a proxy for density. The Spearman rank
coefficient is printed in the upper left corner. The best fit linear function as well as the lines representing ±2σ in both slope and y intercept
are also plotted. There is a mild correlation between density and ICL luminosity such that higher density clusters have a larger amount of
ICL flux.
ICL 33
Fig. 18.— The difference in magnitude between the first and third ranked galaxy versus projected number of galaxies brighter than
Mr = −18.5 within 800 h−170 kpc, which is used as a proxy for density. Clusters with cD galaxies will have larger M3-M1 values. This plot
implies that over time galaxies merge in clusters to make a cD galaxy, and by the time the cD galaxy has formed, the global density is lower.
As discussed in the §6, we assume this is not a selection bias.
34 Krick, Bernstein
Fig. 19.— The difference in magnitude between the first and third ranked galaxy versus ICL luminosity. ICL luminosity shows 1σ error
bars and has been K and evolution corrected. Clusters which have cD galaxies have larger M3 - M1 values and are dynamically older clusters.
There is a mild correlation between dynamic age and ICL luminosity indicating that the ICL evolves at roughly the same rate as the cluster.
ICL 35
Fig. 20.— The flux in galaxies versus the flux in ICL in units of solar luminosities. Errors on ICL luminosity are 1σ. Errors on galaxy
luminosity are 30% as estimated in §5.1.1. Over-plotted is the best fit linear function as well as two lines which represent 2σ errors in both
y-intercept and slope. The Spearman rank coefficient is printed in the upper right. Here galaxy luminosity is assumed to be a proxy for mass,
so we find a significant correlation between mass and ICL flux such that more massive clusters have a larger amount of ICL flux.
36 Krick, Bernstein
Fig. 21.— Cluster mass versus the ICL fraction measured at one quarter of the virial radius. Stars denote the r−band while squares show
B− and diamonds show V−band. Errors on ICL fraction are 1σ as discussed in §5.3. Mass estimates and errors are taken from the literature
as discussed in §A.1 - §A.10. The predictions of Lin & Mohr (2004) and Murante et al. (2004) at the virial radius are shown for comparison.
These represent extrapolations beyond roughly 1×1015 M� in both cases (as marked by the crosses). The roughly constant ICL fraction with
mass can be explained using hierarchical formation by the in-fall of groups with a similar ICL fraction as the main cluster, or by increased
interaction rates with the infall of the groups, or both.
ICL 37
Fig. 22.— Cluster redshift versus ICL fraction measured at one quarter of the virial radius. As in Figure 21, starred symbols denote
the r−band, squares show B−band, and diamonds show V−band fractions. The prediction of Willman et al. (2004) for the ICL fraction as
measured at r200 is shown for comparison. This prediction would increase if measured at smaller radii, such as was used in our measurement.
There is mild evidence for a correlation between redshift and ICL fraction such that ICL fraction grows with decreasing redshift. This trend
is consistent with ongoing ICL formation.
38 Krick, Bernstein
Fig. 23.— Cluster redshift versus ICL color in B − r which has been k corrected and had simple passive evolution applied to it. If a color
gradient is detected in a given cluster then the mean color plotted here is that measured near the center of the profile, weighted slightly
toward the center. There is no trend in redshift with ICL color which leads to the conclusion that the ICL is simply passively reddening.
ICL 39
APPENDIX
the clusters
In order of increasing redshift we discuss interesting characteristics of the clusters and their ICL components. Relevant
papers are listed in Table 1. Relevant figures are 4 - 13.
A4059
A4059 is a richness class 1, Bautz Morgan type I cluster at a redshift of 0.048. There is a clear cD galaxy which is
however offset from the Abell center, likely due to the presence of at least two other bright elliptical galaxies. The cD
galaxy is 0.91 ± .05 magnitudes brighter than the second ranked cluster galaxy. The cD galaxy is at the center of the
Chandra and ASCA mass distributions. Those telescopes detect no hot gas around the other bright ellipticals. This
cluster shows interesting features in it’s X–ray morphology. There appear to be large bubbles, or cavities in the hot gas,
which is likely evidence of past radio galaxy interactions with the ICM (Choi et al. 2004). As additional evidence of past
activity in this cluster, the cD galaxy contains a large dust lane (Choi et al. 2004). M500 (the mass within the radius
where the mean mass density is equal to 500 times the critical density) is calculated by Reiprich & Böhringer (2002) for
A4059 to be 2.82±0.370.34 ×1014h
70 M�.
The color magnitude diagram shows a very tight red sequence. Membership information is taken from Collins et al.
(1995), Colless et al. (2001), and Smith et al. (2004). Using the CMD as an indication of membership, we estimate the
flux in cluster galaxies to be 1.2± .35×1012L�in r and 4.2±1.3×1011L�in B inside of 0.65h−170 Mpc, which is one quarter
of the virial radius of this cluster. In this particular cluster, since the Abell center is not at the true cluster center, and it
is the nearest cluster in our sample, our image does not uniformly cover the entire one quarter of the virial radius. This
estimate is therefore below the true flux in galaxies because we are missing area on the cluster.
Figure 4 shows the relevant plots for this cluster. There is a strong ICL component ranging from 26 - 29 mag arcsec−2
in r centered on the cD galaxy. The total flux in the ICL is 3.4 ± 1.7 × 1011L�in r and 1.2 ± .24 × 1011L�in B, which
makes for ICL fractions of 22± 12% in r and 21± 8% in B. The ICL has a flat color profile with B− r ' 1.7± .08, which
is marginally bluer (0.2 magnitudes) than the RCS. One of the two other bright ellipticals at 0.7h−170 Mpc from the center
has a diffuse component, the other bright elliptical is too close to a saturated star to detect a diffuse component.
A3880
A3880 is a richness class 0, Bautz Morgan type II cluster at a redshift of 0.058. There is a clear cD galaxy in the center
of this cluster, which is 0.52 ± .05 magnitudes brighter than the second ranked galaxy. This cluster is detected in the
ROSAT All Sky Survey, however that survey is not deep enough to show us the shape of the mass distribution. Girardi
et al. (1998b) find a mass for this cluster based on its velocity dispersion of 8.3+2.8−2.1 × 10
14h−170 M�.
The color magnitude diagram shows a clear red sequence. There is possibly another red sequence at lower redshift
adding to the width of the red sequence. Membership information is provided by Collins et al. (1995), Colless et al.
(2001), and Smith et al. (2004). Using the CMD as an indication of membership, we estimate the flux in cluster galaxies
to be 8.6± 2.6× 1011L�in r and 3.8± 1.1× 1011L�in B inside of 0.62h−170 Mpc, which is one quarter of the virial radius
of this cluster.
Figure 5 shows the relevant plots for this cluster. Unfortunately this cluster has larger illumination problems than the
other clusters which can be seen in the greyscale masked image. Nonetheless, there is clearly an r−band ICL component,
although the B−band ICL is extremely faint. The total flux in the ICL is 1.4± 2.3× 1011L�in r and 4.4± 1.5× 1010L�in
B, which makes for ICL fractions of 14±6% in r and 10±6% in B. The ICL has a flat color profile with B−r ' 2.4±1.1,
which is 0.8 magnitudes redder than the RCS.
A2734
A2734 is a richness class 1, Bautz Morgan type III cluster at a redshift of 0.062. The BCG by 0.51± .05 magnitudes is
in the center of this cluster, however there are 2 other large elliptical galaxies 0.55h−170 Mpc and 0.85h
70 Mpc distant from
the BCG. The X–ray gas does confirm the BCG as being at the center of the mass distribution. Those 2 other elliptical
galaxies are not seen in the 44ks ASCA GIS observation of this cluster, however they are confirmed members based on
spectroscopy (Collins et al. 1995; Colless et al. 2001; Smith et al. 2004). M500 is calculated by Reiprich & Böhringer
(2002) for A2734 to be 2.49±0.890.63 ×1014h
70 M�.
The color magnitude diagram shows a clear red sequence, which includes the 3 bright elliptical galaxies. 2df spectroscopy
gives us roughly 80 galaxies in our field of view which we can use to estimate the effectiveness of the biweight fit to the
RCS in finding true cluster members. Of those galaxies with confirmed membership, 94% are determined members with
this method, however 86% of the confirmed non-members are also considered members. This is likely due to how galaxies
were selected for spectroscopy in the 2df catalog. Using the CMD as an indication of membership, we estimate the flux
in cluster galaxies to be 1.2± .36× 1012L�in r and 3.4± 1.0× 1011L�in B inside of 0.60h−170 Mpc, which is one quarter of
the virial radius of this cluster.
Figure 6 shows the relevant plots for this cluster. There is a strong ICL component ranging from 26 - 29 mag arcsec−2
in r centered on the BCG. The total flux in the ICL is 2.8± .47× 1011L�in r and 7.0± 4.7× 1010L�in B, which makes
for ICL fractions of 19± 6% in r and 17± 13% in B. The ICL has a flat to red-ward color profile with B − r ' 2.3± .03,
40 Krick, Bernstein
which is marginally redder than the RCS (0.3 magnitudes). The cluster has a second diffuse light component around one
of the giant elliptical galaxies, .55 h−170 Mpc from the center of the cD galaxy. The third bright elliptical has a saturated
star just 40′′away, so we do not have a diffuse light map of that galaxy.
A2556
A2556 is a richness class 1, Bautz Morgan type II-III cluster at a redshift of 0.087. Despite the Bautz Morgan
classification, this cluster has a clear cD galaxy in the center of the X–ray distribution which is 0.93 ± .05 magnitudes
brighter than any other galaxy in the cluster. The Chandra derived X–ray distribution is slightly elongated toward the
NE where a second cluster, A2554, resides, 1.4h−170 Mpc from the center of A2556. The cD galaxy of A2554 is just on the
edge of our images so we have no information about its low surface brightness component. A2556 and A2554 are a part
of the Aquarius supercluster(Batuski et al. 1999), so they clearly reside in an overdense region of the universe. Given an
X–ray luminosity from Ebeling et al. (1996) and a velocity dispersion from Reimers et al. (1996), we calculate the virial
mass of A2556 to be 2.5± 1.1× 1015h−170 M�.
The red sequence for this cluster is a bit wider than in other clusters. The one sigma width to a biweight fit is 0.38
magnitudes in B-r which is approximately 30% larger than in the rest of the low-z sample. This extra width is not caused
by only a few galaxies, instead the entire red sequence appears to be inflated. This is probably caused by the nearby
A2554 which is at z=0.11 (Struble & Rood 1999). This is close enough in redshift space that we cannot separate out
the 2 red sequences in our CMD. We have roughly 30 redshifts for A2556 from Smith et al. (2004), Caretta et al. (2004),
and Batuski et al. (1999) which are also unable to differentiate between the clusters. Using the CMD as an indication of
membership, we estimate the flux in cluster galaxies to be 1.3 ± .38 × 1012L�in r and 3.3 ± 1.0 × 1011L�in B inside of
0.65h−170 Mpc, which is one quarter of the virial radius of this cluster.
Figure 7 shows the relevant plots for this cluster. There is an r−band ICL component ranging from 27 - 29 mag arcsec−2
in r centered on the cD galaxy. The B−band ICL is extremely faint, barely above or detection threshold. Although we
were able to fit a profile to the B−band diffuse light, all points on the medium sized mask are below 29 mag arcsec−2.
The total flux in the ICL is 7.6± 6.6× 1010L�in r and 1.4± 1.4× 1010L�in B, which makes for ICL fractions of 6± 5%
in r and 4 ± 4% in B. Although Figure 7 shows a color profile, we do not assume anything about the profile shape due
to the low SB level of the B−band. We take the B − r color from the innermost point to be 2.1 ± 0.4, which is fully
consistent with the color of the RCS.
A4010
A4010 is a richness class 1, Bautz Morgan type I-II cluster at a redshift of 0.096. This cluster has a cD galaxy in the
center of the galaxy distribution, which is 0.7 ± .05 magnitudes brighter than the second ranked galaxy. There is only
ROSAT All Sky Survey data for this cluster and no other sufficiently deep X–ray observations to show us the shape of
the mass distribution. There are weak lensing maps which put the center of mass of the cluster at the same position as
the cD galaxy, and elongated along the same position angle as the cD galaxy (Cypriano et al. 2004). Muriel et al. (2002)
find a velocity dispersion of 743 ± 140 for this cluster which is 15% larger than found by Girardi et al. (1998b), where
those authors find a virial mass of 3.8±1.61.2 ×1014h
70 M�.
The color magnitude diagram for A4010 is typical among the sample with a clear red sequence. A few redshifts exist
in the literature which help define the red sequence (Collins et al. 1995; Katgert et al. 1998). Using the CMD as an
indication of membership, we estimate the flux in cluster galaxies to be 1.2± .4× 1012L�in r and 3.5± 1.0× 1011L�in B
inside of 0.75h−170 Mpc, which is one quarter of the virial radius of this cluster.
Figure 8 shows the relevant plots for this cluster. There is an elongated ICL component ranging from 25.5 - 28 mag
arcsec−2 in r centered on the cD galaxy. The total flux in the ICL is 3.2± 0.7× 1011L�in r and 7.7± 2.8× 1010L�in B,
which makes for ICL fractions of 21 ± 8% in r and 18 ± 8% in B. The ICL has a significant red-ward trend in its color
profile with an average color of B − r ' 2.1± 0.1, which is marginally redder (0.2 magnitudes) than the RCS.
A3888
A3888 is discussed in great detail in paper I. In review, A3888 is a richness class 2, Bautz Morgan type I-II cluster
at a redshift of 0.151. This cluster has no cD galaxy; instead the core is comprised of 3 distinct sub-clumps of multiple
galaxies each. At least 2 galaxies in each of the subclumps are confirmed members based on velocities (Teague et al.
1990; Pimbblet et al. 2002). The brightest cluster galaxy is only 0.12± .04 magnitudes brighter than the second ranked
galaxy. XMM contours show an elongated distribution centered roughly in the middle of the three clumps of galaxies.
Reiprich & Böhringer (2002) estimate mass from the X–ray luminosity to be M200 = 25.5±10.57.4 × 1014h
70 M�, where r200
= 2.8h−170 Mpc. This is consistent with the mass estimate from the published velocity dispersion of 1102±
107 (Girardi &
Mezzetti 2001).
There is a clear red sequence of galaxies in the CMD of A3888. Using the CMD as an indication of membership, we
estimate the flux in cluster galaxies to be 3.0±0.9×1012L�in r and 7.2±2.2×1011L�in B inside of 0.92h−170 Mpc. We also
determine galaxy flux using the Driver et al. (1998) luminosity distribution, which is based on the statistical background
subtraction of non-cluster galaxies, to be 4.3 ± 0.7 × 1012L�in the r−band and 3.4 ± 0.6 × 1012L�in V . The difference
in these two estimates is likely due to uncertainties in our membership identification (of order 30%) and difference in
detection thresholds of the two surveys.
ICL 41
Figure 9 shows the relevant plots for this cluster. There is a centralized ICL component ranging from 26 - 29 mag
arcsec−2 in r despite the fact that there is no cD galaxy. The total flux in the ICL is 4.4 ± 2.1 × 1011L�in r and
8.6 ± 2.5 × 1010L�in B, which makes for ICL fractions of 13 ± 5% in r and 11 ± 3% in B. The ICL has a red color
profile with an average color of V − r ' 0.5 ± 0.1, which is marginally redder (0.2 magnitudes) than the RCS. There is
also a diffuse light component surrounding a group of galaxies that is 1.4 h−170 Mpc from the cluster center which totals
1.7± 0.5× 1010L�in V and 2.6± 1.2× 1010L�in r and has a color consistent with the main ICL component.
A3984
A3984 is an interesting richness class 2, Bautz Morgan type II-III cluster at a redshift of 0.181. There appear to be 2
centers of the galaxy distribution. One around the BCG, and one around a semi-circle of ∼ 5 bright ellipticals which are
1h−170 Mpc north of the BCG. The BCG and at least one of the other bright ellipticals are at the same redshift (Collins et al.
1995). To determine if these 2 centers are part of the same redshift structure, we split the image in half perpendicular to
the line bisecting the 2 regions, and plot the cumulative distributions of V − r galaxy colors. A KS test reveals that these
2 regions have an 89% probability of being drawn from the same distribution. Without X–ray observations we do not
know where the mass in this cluster resides. There is a weak lensing map of just the northern region of the cluster which
does show a centralized mass distribution, but does not include the southern clump (Cypriano et al. 2004). The BCG is
0.57± .04 magnitudes brighter than the second ranked galaxy. We use a velocity dispersion from the lensing measurement
to determine a mass of 31± 10× 1014h−170 M�.
There is a clear red sequence of galaxies in the CMD of A3984. Using the CMD as an indication of membership, we
estimate the flux in cluster galaxies to be 2.0± 0.6× 1012L�in r and 4.4± 1.3× 1011L�in B inside of 0.87h−170 Mpc, which
is one quarter of the virial radius of this cluster.
Figure 10 shows the relevant plots for this cluster. There are 2 clear groupings of diffuse light. We can only fit a profile to
the ICL which is centered on the BCG. We stop fitting that profile before it extends into the other ICL group (∼ 600kpc)
in an attempt to keep the fluxes separate. The total flux in the ICL is 2.2 ± 1.0 × 1011L�in r and 6.2 ± 2.1 × 1010L�in
B, which makes for ICL fractions of 10± 6% in r and 12± 6% in B. The ICL becomes distinctly bluer with radius and
is bluer at all radii than the RCS with an average color of V − r ' −0.2± 0.4 (0.5 magnitudes bluer than the RCS).
A0141
A0141 is a richness class 3, Bautz Morgan type III cluster at a redshift of 0.23. True to its morphological type, this
cluster has no cD galaxy, instead it has 4 bright elliptical galaxies, each at the center of a clump of galaxies, the brightest
one of which is 0.42± .04 magnitudes brighter than the second brightest. The center of the cluster, as defined by ASCA
observations and a weak lensing map (Dahle et al. 2002), is near the northernmost clumps of galaxies. The distribution is
clearly elongated north-south, it is therefore possible that the other bright ellipticals are in-falling groups along a filament.
M200 from the lensing map is 18.9±1.10.9 ×1014 h
70 M�.
There is a clear red sequence of galaxies in the CMD of A0141. Using the CMD as an indication of membership, we
estimate the flux in cluster galaxies to be 3.2± 1.0× 1012L�in r and 5.4± 1.6× 1011L�in B inside of 0.94 h−170 Mpc, which
is one quarter of the virial radius of this cluster.
Figure 11 shows the relevant plots for this cluster. There are 3 clear groupings of diffuse light which do not have a
common center, although 1 of these ICL peaks does include 2 clumps of galaxies. We are unable to fit a single centralized
profile to this ICL as the three clumps are too far separated. The total flux in the ICL as measured in manually placed
elliptical annuli is 3.5± .9× 1011L�in r and 3.4± 1.1× 1010L�in B, which makes for ICL fractions of 10± 4% in r and
6 ± 3% in B. We estimate the color of the ICL to be V − r ' 1.0 ± 0.8, which is significantly redder (0.6 magnitudes)
than the RCS. We have no color profile information.
AC114
AC114 (AS1077) is a richness class 2, Bautz Morgan type II-III cluster at a redshift of 0.31. The brightest galaxy is only
0.28± .04 magnitudes brighter than the second ranked galaxy. The galaxy distribution is elongated southeast to northwest
(Couch et al. 2001) as is the Chandra derived X–ray distribution. The X–ray gas shows a very irregular morphology, with
a soft X–ray tail stretching toward a mass clump in the southeast which is also detected in a lensing map (De Filippis
et al. 2004; Campusano et al. 2001). The X–ray gas is roughly centered on a bright elliptical galaxy, however the tail is
an indication of a recent interaction. There is a clump of galaxies, 1.6h−170 Mpc northwest of the BCG, which looks like a
group or cluster with its own cD-like galaxy which is not targeted in either the X–ray or lensing (strong) observations.
Only one of these galaxies has redshifts in the literature, and it is a member of AC114. Without redshifts, we cannot
know definitively if these galaxies are a part of the same structure, however their location along the probable filament
might be evidence that they are part of the same velocity structure. As this cluster is not in dynamical equilibrium, mass
estimates from the X–ray gas come from B-model fits to the surface brightness distribution. De Filippis et al. (2004) find
a mass within 1h−170 Mpc of 4.5 ± 1.1 × 10
14h−170 M�. A composite strong and weak lensing analysis agree with the X–ray
analysis within 500h−170 kpc, but they do not extend out to larger radii (Campusano et al. 2001). Within the virial radius,
(Girardi & Mezzetti 2001) find a mass of 26.3+8.2−7.1 × 10
14h−170 M�.
This cluster, in relation to lower-z clusters, is a prototypical example of the Butcher-Oemler effect. There is a higher
fraction of blue, late-type galaxies at this redshift, than in our lower-z clusters, rising to 60% outside of the core region
42 Krick, Bernstein
(Couch et al. 1998). This is not only evidenced in the morphologies, but in the CMD, which nicely shows these blue
member galaxies. We adopt the Andreon et al. (2005) luminosity function for this cluster based on an extended likelihood
distribution for background galaxies. Integrating the luminosity distribution from very dim dwarf galaxies (MR = −11.6)
to infinity gives a total luminosity for AC114 of 1.5± 0.2× 1012L�in r and 1.9± 1.2× 1011L�in B inside of 0.9h−170 Mpc,
which is one quarter of the virial radius of this cluster. For the purpose of comparison with other clusters, we adopt the
cluster flux from the CMD, which gives 1.8 ± 0.5 × 1012L�in r and 2.3 ± 0.7 × 1011L�in B inside of one quarter of the
virial radius of this cluster. The differences in these estimates are likely due to uncertainties in membership identification
and differing detection thresholds of the two surveys.
Figure 12 shows the relevant plots for this cluster. There is a centralized ICL component ranging from 27.5 - 29 mag
arcsec−2 in r, in addition to a diffuse component around the group of galaxies to the northwest of the BCG. The total flux
in the ICL is 2.2± 0.4× 1011L�in r and 3.8± 7.9× 1010L�in B, which includes the flux from the group as measured in
elliptical annuli. The ICL fraction is 11±2% in r and 14±3% in B. The ICL has a flat color profile with V −r ' 0.1±0.1,
which is marginally bluer (0.4 magnitudes) than the RCS.
AC118 (A2744)
AC118 (A2744) is a richness class 3, Bautz Morgan type III cluster at a redshift of 0.31. This cluster has 2 main clumps
of galaxies separated by 1h−170 Mpc, with a third bright elliptical in a small group which is 1.2h
70 Mpc distant from the
center of the other clumps. The BCG is 0.23 ± .04 magnitudes brighter than the second ranked galaxy. The Chandra
X–ray data suggests that there are probably 3 clusters here, at least 2 of which are interacting. The gas distribution,
along with abundance ratios, suggests that the third, smaller group might be the core of one of the interacting clusters
which has moved beyond the scene of the interaction where the hot gas is detected. From velocity measurements Girardi
& Mezzetti (2001) also find 2 populations of galaxies with distinctly different velocity dispersions. The presence of a large
radio halo and radio relic are yet more evidence for dynamical activity in this cluster (Govoni et al. 2001). Mass estimates
for this cluster range from ∼ 3×1013M� from X–ray data to ∼ 3×1015M� from the velocity dispersion data. This cluster
clearly violates assumptions of sphericity and hydrostatic equilibrium, which is leading to the large variations. The two
velocity dispersion peaks have a total mass of 38± 37× 1014 h−170 M�; we adopt this mass throughout the paper.
AC118, at the same redshift as AC114, also shows a significant fraction of blue galaxies, which leads to a wider red cluster
sequence(1σ = 0.3 magnitudes), than at lower redshifts. We adopt the Busarello et al. (2002) R and V−band luminosity
distributions based on photometric redshifts and background counts from a nearby, large area survey. Integrating the
luminosity distribution from very dim dwarf galaxies (MR = −11.6) to infinity gives a total luminosity for AC118 of
4.5 ± .2 × 1011L� in V and 4.2 ± .4 × 1012L� in the r−band inside of 0.25rvirial. For the purpose of comparison with
other clusters, we adopt the cluster flux from the CMD, which gives 5.4 ± 1.6 × 1011L�in B and 4.4 ± 0.1 × 1012L�in r
inside of 0.94h−170 Mpc, which is one quarter of the virial radius of this cluster.
Figure 13 shows the relevant plots for this cluster. There are at least two, if not three groupings of diffuse light which
do not have a common center. The possible third is mostly obscured behind the mask of a saturated star. We are unable
to fit a centralized profile to this ICL. The total flux in the ICL as measured in manually placed elliptical annuli is
7.0± 1.0× 1011L�in r and 6.7± 1.7× 1010L�in B, which makes for ICL fractions of 14± 5% in r and 11± 5% in B. We
estimate the color of the ICL to be V − r ' 1.0 ± 0.8, which is significantly redder (0.6 magnitudes) than the RCS. We
have no color profile information.
	Introduction
	The Sample
	Observations
	Reduction
	Object Detection
	Object Removal & Masking
	Stars
	Galaxies
	Results
	Cluster Properties
	Cluster Membership & Flux
	Dynamical Age
	Global Density
	ICL properties
	Surface brightness profile
	 ICL Flux 
	 ICL Fraction 
	Color
	ICL Substructure
	Groups
	Accuracy Limits
	Discussion
	ICL flux
	ICL Flux vs. M3-M1
	ICL fraction
	ICL fraction vs. Mass
	ICL fraction vs. Redshift
	ICL color
	Profile Shape
	Conclusion
	The Clusters
	A4059
	A3880
	A2734
	A2556
	A4010
	A3888
	A3984
	A0141
	AC114
	AC118 (A2744)

  We have measured the flux, profile, color, and substructure in the diffuse
intracluster light (ICL) in a sample of ten galaxy clusters with a range of
mass, morphology, redshift, and density. Deep, wide-field observations for this
project were made in two bands at the one meter Swope and 2.5 meter du Pont
telescope at Las Campanas Observatory. Careful attention in reduction and
analysis was paid to the illumination correction, background subtraction, point
spread function determination, and galaxy subtraction. ICL flux is detected in
both bands in all ten clusters ranging from 7.6 x 10^{10} to 7.0 x 10^{11}
h^{-1} solar luminosities in r and 1.4 x 10^{10} to 1.2 x 10^{11} h^{-1} solar
luminosities in the B-band. These fluxes account for 6 to 22% of the total
cluster light within one quarter of the virial radius in r and 4 to 21% in the
B-band. Average ICL B-r colors range from 1.5 to 2.8 mags when k and evolution
corrected to the present epoch. In several clusters we also detect ICL in group
environments near the cluster center and up to ~1 h^{-1} Mpc distant from the
cluster center. Our sample, having been selected from the Abell sample, is
incomplete in that it does not include high redshift clusters with low density,
low flux, or low mass, and it does not include low redshift clusters with high
flux, mass, or density. This bias makes it difficult to interpret correlations
between ICL flux and cluster properties. Despite this selection bias, we do
find that the presence of a cD galaxy corresponds to both centrally
concentrated galaxy profiles and centrally concentrated ICL profiles. This is
consistent with ICL either forming from galaxy interactions at the center, or
forming at earlier times in groups and later combining in the center.

Introduction
	The Sample
	Observations
	Reduction
	Object Detection
	Object Removal & Masking
	Stars
	Galaxies
	Results
	Cluster Properties
	Cluster Membership & Flux
	Dynamical Age
	Global Density
	ICL properties
	Surface brightness profile
	 ICL Flux 
	 ICL Fraction 
	Color
	ICL Substructure
	Groups
	Accuracy Limits
	Discussion
	ICL flux
	ICL Flux vs. M3-M1
	ICL fraction
	ICL fraction vs. Mass
	ICL fraction vs. Redshift
	ICL color
	Profile Shape
	Conclusion
	The Clusters
	A4059
	A3880
	A2734
	A2556
	A4010
	A3888
	A3984
	A0141
	AC114
	AC118 (A2744)
