Issue |
A&A
Volume 507, Number 1, November III 2009
|
|
---|---|---|
Page(s) | 147 - 157 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/200912299 | |
Published online | 01 October 2009 |
A&A 507, 147-157 (2009)
JKCS 041: a colour-detected
galaxy cluster at
with deep potential well as confirmed by X-ray data
S. Andreon1 - B. Maughan2 - G. Trinchieri1 - J. Kurk3
1 - INAF - Osservatorio Astronomico di Brera, via Brera 28, 20121
Milano, Italy
2 - Department of Physics, University of Bristol, Tyndall Ave, Bristol
BS8 1TL, UK
3 - Max-Planck-Institut fur Astronomie, Konigstuhl 17, 69117
Heidelberg, Germany
Received 8 April 2009 / Accepted 24 September 2009
Abstract
We report the discovery of JKCS 041, a massive near-infrared
selected cluster of galaxies at .
The cluster was originally discovered using a modified red-sequence
method and also detected in follow-up Chandra data as an extended X-ray
source. Optical and near-infrared imaging data alone allow us to show
that the detection of JKCS 041 is secure, even in the absence
of
the X-ray data. We investigate the possibility that JKCS 041
is
not a galaxy cluster at
,
and find other explanations unlikely. The X-ray detection and
statistical arguments rule out the hypothesis that JKCS 041 is
actually a blend of groups along the line of sight, and we find that
the X-ray emitting gas is too hot and dense to be a filament projected
along the line of sight. The absence of a central radio source and the
extent and
morphology of the X-ray emission argue against the possibility that the
X-ray emission comes from inverse Compton scattering of
CMB photons by a radio plasma. The cluster has an X-ray core
radius of
36.6 +8.3-7.6 arcsec
(about 300 kpc), an X-ray temperature of
7.4+5.3-3.3 keV,
a bolometric X-ray luminosity within R500
of
1044 erg s-1,
and an estimated mass of M500=2.9+3.8-2.4
,
the last derived under the usual (and strong) assumptions. The cluster
is composed of 16.4
6.3 galaxies within 1.5 arcmin (750 kpc)
brighter than
mag.
The high redshift of JKCS 041 is determined
from the detection colour, from the detection of the cluster in a
galaxy sample formed by
galaxies
and from a photometric redshift based on 11-band spectral energy
distribution fitting. By means of the latter we find the
cluster
redshift to be
1.84< z<2.12 at
68% confidence.
Therefore, JKCS 041 is a cluster of galaxies at
with a deep
potential well, making it the most distant cluster with extended X-ray
emission known.
Key words: galaxies: evolution - galaxies: clusters: general - galaxies: clusters: individual: JKCS 041 - cosmology: dark matter - X-rays: galaxies: clusters - methods: statistical
1 Introduction
Clusters of galaxies are known to harbour red galaxies out to the highest redshifts explored thus far, z=1.45 (Stanford et al. 2006). They owe their colour mostly to their old stellar populations: their luminosity function is passively evolving (de Propris et al. 1999; Andreon 2006a; Andreon et al. 2008) and their colour-magnitude relation evolves in slope, intercept and scatter as expected for a passive evolving population (e.g. Stanford et al. 1999; Kodama et al. 1998). The presence of red galaxies characterises clusters irrespectively of the way they are selected: X-ray selected clusters show a red sequence (see, e.g. Andreon et al. 2004a, 2005, for XMM-LSS cluster samples at 0.3<z<1.2; Ebeling et al. 2007 for MACS; whose red sequence is reported in Stott et al. 2007; and Andreon 2008; see also Stanford et al. 2005; Lidman et al. 2008; Mullis et al. 2005, for some other individual clusters). The first Sunyaev-Zeldovich selected clusters (Staniszewski et al. 2009) have been confirmed thanks to their red sequence, and their photometric redshifts were derived from its colour. The same is often true for shear-selected clusters (e.g. Wittman et al. 2006), starting with the first such example (Wittman et al. 2001).
It is now established that bright red galaxies exist at the appropriate frequency in high redshift clusters and the discussion has now shifted to the faint end of the red population. Current studies aim at establishing whether their abundance evolves with lookback time, with proponents divide into opposite camps (e.g. Andreon 2008; Lidman et al. 2008; Crawford et al. 2009; Tanaka et al. 2008; vs. Gilbank & Balogh 2008; Stott et al. 2007). However, these galaxies are too faint for the purpose of discovering clusters, and thus such discussion is not relevant here.
On the contrary, bright red galaxies are a minority in the field population. Consequently, selections based on galaxy colour enhance the contrast of the large red galaxy population in clusters relative to bluer field galaxies.
The Balmer break is a conspicuous characteristic feature of galaxy spectra. Red colours are observed when a galaxy has a prominent Balmer break and is at the appropriate redshift for the filter pair used. In some circumstances a galaxy may look red, when another spectral break (e.g. Lyman break) falls between the filter pair, but these galaxies are numerically few in actual samples and are not clustered as strongly as the galaxies in clusters. Therefore, their clustering cannot be mistaken as a cluster detection. Indeed, if they were found to be so strongly clustered it would be an interesting discovery in itself. The colour index thus acts as a (digital) filter and removes galaxies with a spectral energy distribution inconsistent with the expected one. One can then select against objects at either a different redshift or having unwanted colours, making the colour selection a very effective way of detecting clusters. Red-sequence-like algorithms, pioneered by Gladders & Yee (2000) for detecting clusters rely precisely on identifying a spatially localised overdensity of galaxies with a pronounced Balmer break.
Several implementations of red-sequence-like algorithms have been used
to detect clusters (e.g. Gladders & Yee 2000; Goto
et al. 2002; Koester
et al. 2007),
each one characterised by different assumptions on the cluster model
(i.e. on the expected properties of the ``true'' cluster). For
example, some models require clusters to follow the
Navarro et al. (1997) radial
profile with a predicted radius scale. Alternatively the scale (or form
of the profile) can be left free (or only weakly bounded).
To enlarge the redshift baseline in such cluster surveys, one
is
simply required to change filters in order to ``follow'' the Balmer
break to higher and higher redshifts. One version of these algorithms
has been applied in a series of papers by Andreon et al.' to
cover
the largest redshift range sampled in one study. In
Andreon (2003) the
technique was applied to the nearby (z<0.3)
universe using SDSS g and r filters.
With the R and z' filters
the medium-distant universe (
)
was probed (Andreon et al. 2004a,b, 2005, 2008, note
that the same bands are used by the red sequence survey, Gladders
& Yee 2005).
To sample the
universe,
infrared bands must be used for the ``red'' filter, because the Balmer
break moves into the z' band at
.
This is shown by Andreon et al. (2005), who
were able to detect XLSSC 046 (called ``cluster h''
in that paper) at z=1.22 using z'
and J, but we missed it in R
and z'. XLSSC 046 was later
spectroscopically confirmed in Bremer et al. (2006).
Cluster detection by red sequence-like method is observationally cheap:
it is possible to image large ( deg2 per
exposure) sky regions at sufficient depth to detect clusters up
to z=1, with short (
ks)
exposure times on 4 m ground-based telescopes. All-sky X-ray
surveys such as the RASS also provide efficient cluster detections,
although they do require more expensive space-based observations. For
distant clusters, currently operational X-ray observatories are less
efficient than red-sequence methods due to the longer exposure times
required (e.g. >10 ks) and smaller fields of
view
(1/9 deg2 at most). As a
result, red-sequence-like
studies are able to follow the cluster mass function down to lower mass
limits than is possible with X-ray observations (e.g. Andreon
et al. 2005).
On the other
hand, X-ray detected clusters are less affected by confusion issues,
i.e. by the possibility that the detected structure is
a line-of-sight blend of smaller structures. Cluster detection by
gravitational shear, meanwhile, requires deeper optical data than
red-sequence-like algorithms. This is because shear studies require the
measuement of subtler quantities (small distortions in shape) of
fainter galaxies. Furthermore, detection by shear is affected by
confusion (we will return to this issue in Sect. 3.6), and
thus
may be of limited use for detecting samples of clusters. However, shear
measurements offer a more direct probe of cluster mass, provided the
shear detection has a sufficient signal to noise to perform the
measurement in survey data (although this is currently not common, see
e.g. Schirmer et al. 2007).
In this paper, we present the detection of the very distant, cluster
JKCS 041
obtained by adopting redder (J and K) filters
to follow the Balmer break to even higher redshifts. Since the Balmer
break enters the J filter at
,
J-K>2.3 mag (in
the Vega system) is a very effective criterion (Saracco
et al. 2001; Franx
et al. 2003) for
selecting
galaxies.
The spectroscopic study by Reddy et al. (2005), by
Papovich et al. (2006) and by
Kriek et al. (2008), all
confirme that the J-K>2.3 mag
criterion selects mainly galaxies at
.
The paper is organised as follow: Sect. 2 presents the original cluster discovery. In Sect. 3 we reinforce the cluster detection using different methods and we determined the cluster photometric redshift. In that section, we also show that JKCS 041 is not a blend of two (or more) groups along the line of sight. JKCS 041 is also X-ray detected in follow-up Chandra observations. Section 4 describes these data, confirming the cluster detection, and presents our measurements of basic cluster properties (core radius, luminosity, temperature). We also show here that other possible interpretations of the X-ray emission (unlikely a priori) do not match our data. After a short discussion (Sect. 5), Sect. 6 summarises the results.
We adopt ,
and H0=70 km s-1 Mpc-1.
The scale, at z=1.9, is
8.4 kpc arcsec-1.
Magnitudes are quoted in the photometric system in which they were
published (Vega for near-infrared photometry, AB for optical
photometry), unless stated otherwise.
![]() |
Figure 1:
Number density image of a region of 146 Mpc2
(0.16 deg2) area centred on
JKCS 041. In the left panel, only
2.1<J-K<2.5 mag
galaxies are considered, and all the ancillary photometry ignored. This
shows the original cluster detection. In the central panel,
we
have discarded foreground galaxies, as identified by their spectral
energy distribution (SED), using spectroscopy, optical and
near-infrared photometry. In the right panel, we
only keep galaxies with SEDs similar to the Grasil 1.5 Gyr old
ellipticals at z=1.9
and we also use Spitzer
photometry. JKCS 041, at the centre of each panel, is clearly
detected in all images, independently of the filtering applied. The
images have been smoothed with a Gaussian with |
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2 Near-infrared UKIDSS data and cluster discovery
JKCS 041 was initially detected in 2006 using J and K UKIRT Infrared Deep Sky Survey (UKIDSS, Lawrence et al. 2007) Early Data Release (Dye et al. 2006) as a clustering of sources of similar colour using our own version (Andreon 2003; Andreon et al. 2004a,b) of the red-sequence method (Gladders & Yee 2000). UKIDSS data used here are complete (5 sigma, point sources, 2 arcsec aperture) to K=20.7 mag and J=22.2 mag (Warren et al. 2008).
Basically, all non-Bayesian cluster detection algorithms (including ours) compute a p-value (called significance in CIAO, the Chandra software package, and detection likelihood in XMMSAS). This is sometimes described as the probability of rejecting the null hypothesis ``no cluster is there'', for a set of parameters. In our case, these parameters are sky location, red-sequence colour, cluster size, red-sequence colour width and limiting magnitude. The N-dimensional volume of the parameter space is explored iterating on all (or many) values of the parameters. For the following analysis, we used a regular grid of parameter values.
JKCS 041 is detected with a p-value of 10-11
at J-K=2.3 mag
and RA, dec = (36.695, -4.68) on a scale of
1 arcmin and with a red-sequence width of 0.2 mag.
This would
correspond to detection
in the classical hypothesis testing sense (assuming
a Gaussian distribution). The left panel of Fig. 1 shows the
spatial distribution of the number density of galaxies with
2.1<J-K<2.5 centred
on JKCS 041. The large amount
of white/light-gray space qualitatively shows that the detection is
unlikely to be produced by chance background fluctuations.
Since the discovery of JKCS 041, the UKIDSS data
release catalogue (Warren et al. 2008) has been released. We
make
use of that data here, together with images that we mosaiced from Early
Data Release stacks (Dye et al. 2006).
The detection just described makes a very limited use of the available data. In the next sections we show how much we can infer from optical, near-infrared and IRAC data to characterise JKCS 041 in absence of the X-ray data. These methods are of interest for clusters for which X-ray data are not available, or, even worse, their expected X-ray flux is too low to be detected in a reasonable exposure time. The analysis of JKCS 041 X-ray observations is presented in Sect. 4.
3 What can we learn about JKCS 041 without X-ray data?
Multiwavelength coverage is available for JKCS 041 from a) the
Canada-France-Hawaii Telescope
Legacy Survey (CFHTLS, hereafter) Deep Survey 1 field;
b) the Swire (Lonsdale et al. 2003) fields;
and c) the VVDS spectroscopic field.
CFHTLS gives images (available at the Canadian Astronomy Data Centre)
and Ilbert et al. (2006) give
catalogues in five bands: u*,g',r',i',z'.
Spitzer
gives images and catalogues in four bands ([3.6], [4.5], [5.8]
and [8]). We used Spitzer images, as presented in Andreon (2006a), and
the Spitzer catalogue, as distributed by Surace et al. (2005), the
latter taken (in place of Andreon 2006a
catalogue) to make our work easier to reproduce. Le Fèvre
et al. (2005) give
VVDS spectroscopic redshifts in the general area of JKCS 041
for about 104 galaxies.
However, cluster members are not included in the spectroscopic
catalogue, because they are fainter than the VVDS limiting magnitude (
). We make use of only those
VVDS redshifts that are considered reliable (
).
In order to enhance the cluster detection, we used the above data in two ways: a) to remove foreground objects from the sample, i.e. galaxies that are at much lower (photometric) redshift or; b), to retain galaxies whose observed spectral energy distribution (SED, hereafter) fits that of old (red) galaxies at z=1.9.
3.1 Removing foreground objects
By combining near-infrared photometry, optical photometry and
VVDS spectroscopic data we can remove foreground galaxies. We
flagged as foreground every galaxy whose SED includes a detection in at
least five filters and matches the SED of at least two galaxies with
VVDS redshift z<1.6.
We require a match to two VVDS galaxies (instead of just one)
to
make our flagging more robust against VVDS galaxies with
(potential) bad photometry in our catalogues (for example, because of a
deblending problem). We define two SEDs as matching if they differ by
less than 0.05 mag in each colour index (u*-g',
g'-r', r'-i',
i'-z', z'-J,
J-K).
The advantage of this approach, compared to photometric redshift
estimates, is that all systematics (due to seeing effects, photometric
calibration, templates mismatches, etc.) cancel out in the
comparison. The trial sample is from the very same image (and
catalogue) as the measured sample, and shares its idiosyncrasies.
Furthermore, the above approach does not suffer from redshift
degeneracies, which affect photometric redshifts (
), which attempt an inversion
by deriving
from SEDs.
The central panel of Fig. 1 shows the spatial distribution of the galaxies whose SEDs do not resemble VVDS z<1.6 galaxies. The noise in the map is reduced after the removal of these foreground galaxies, while JKCS 041 is still prominent. Since galaxy positions are not used to decide whether a galaxy is in the foreground, the spatial structure we see in the image is not a spurious feature of the foreground removal.
![]() |
Figure 2:
The J-K
colour distribution of galaxies within 1.5 arcmin
(750 kpc)
of the cluster centre is shown by the solid histogram. The same
distribution measured in the control field (a 1.5<r<15 arcmin
annulus) and normalised to the cluster area is shown by the dashed
histogram with error bars. Foregound galaxies were removed in both
cases. A clear excess is seen at
|
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Figure 2 shows the colour distributions of galaxies not resembling VVDS galaxies at z<1.6, and brighter than K=20.7, for two different regions: within a 1.5 arcmin radius (750 kpc) from the cluster centre, and in a control annulus from 1.5 to 15 arcmin in radius. The latter distribution was normalised to the area of the cluster region. The control histogram gives the expected number of galaxies unrelated to the cluster (those in the foreground but not identified as resembling z<1.6 VVDS galaxies, plus those in the background). More precisely, this second histogram is the maximum likelihood estimate of the true value of the background. To determine the significance of the cluster detection, we used the Bayesian methods introduced in Andreon et al. (2006b) and used in Andreon et al. (2008) to model the same problem. This removes the approximation of the maximum likelihood estimate (i.e. we allow the background to be as uncertain as the data allow, instead of assuming a perfect knowledge of it). The colour distribution of the background data was fit with a Pearson type IV distribution (that allows a larger flexibility than a Gaussian, allowing non-zero skewness and excess kurtosis) and the colour distribution of galaxies in the cluster region were fit with the background model plus a Gaussian. The intensities of these processes are Poisson, with the obvious constraint that the background rate (i.e. intensity divided by the solid angle) in the cluster region and the control region are the same. As priors, we took uniform distributions for all parameters in their plausible ranges and zero outside them (avoiding, for example, normal distributions with negative variances, a colour scatter lower than the colour errors, negative numbers, etc.). A Marchov-Chains Monte Carlo (Metropolis et al. 1953) with a Metropolis et al. (1953) sampler was used to perform the stochastic computation.
![]() |
Figure 3: The
black jagged
curve shows the posterior probability of the number of
JKCS 041
members. Also plotted is a Gaussian with the same mean and standard
deviation (red curve). Note that the Gaussian is a poor approximation
to the probability distribution at low
|
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The (blue) curves and yellow shaded regions in Fig. 2 show the
best-fitting models and
the highest posterior 68% confidence intervals, respectively.
There is a clear excess
above the background, at mag.
This is quantified in Fig. 3, which
shows the probability distribution of the number of cluster galaxies;
there are about
6.3 cluster galaxies. We note that the probability distribution is not
normal (the red curve is a Gaussian with matching mean and
standard deviation), especially at low
values.
The peak of the colour distribution,
mag,
is slighly bluer than the detection colour, J-K=
2.3 mag, because of the presence of blue members, and because
we attribute, for this plot only, J=22.2 mag
to galaxies undetected in the J band.
A second peak in the colour distribution is visible at mag.
Our model does not account for this feature (supposing it to be real,
and not to be another cluster/group on the line of sight), because we
have not allowed two peaks in the colour distribution of cluster
galaxies. If we
added a second Gaussian component in the cluster model, the total
number of cluster galaxies is 19.3
6.3, i.e. there are three additional galaxies, not accounted
for
in our simpler cluster model. With this exception (a half
sigma
difference), all of the inferences based on the simpler model are
unchanged.
3.2 Detection probability
In Sect. 2 we computed the cluster ``detection significance'' and we found 10-11. This number is technically known as p-value, and it is the quantity most often quoted in astronomical papers to measure the strength of a detection. By definition, a p-value is the probability of observing, under the null hypothesis, a value at least as extreme as the one that was actually observed. Readers willing to evaluate the strength of our cluster detection compared to other cluster detections should use our p-value. The detection of JKCS 041 cluster is as ``sure'' as other published 10-11 detections.
Readers desiring to use Bayesian evidence are asked to follow
us
along a longer path. We have some data (those mentioned above) and we
want to know the relative probability (often called evidence hereafter)
of two models: one that claims ``a cluster is there'', and another that
claims ``no cluster is there''. To evaluate this, we
simply
need to compute these two probabilities and calculate their ratio. The
two models (hypotheses) are nested: ``no cluster is there'' is
a
(mathematical) special case of ``at least one cluster is there'', when
all clusters have precisely zero members each.
We assume a priori equal probabilities for the two hypotheses,
to
express our indifference between the two hypotheses in the absence of
any data. When hypotheses are nested, as in our case, the ratio above
simplifies to the Savage-Dickey density ratio (see Trotta 2007,
for an astronomical introduction). The latter ratio is computationally
easier to calculate, because it is the value of the posterior at the
null hypothesis (i.e. at
in Fig. 3)
divided by the prior probability of that value. For a uniform prior
on
between 0 and 50, we find a ratio of 1.9
10-2.
Therefore the probability that a cluster is there is about
50 times larger than the probability that none is there. This
constitutes strong evidence on the Jeffreys (1961) scale
(see Liddle 2004,
for an astronomical introduction to the scale). Our evidence ratio
implies that only one in 50 clusters detected at the claimed
significance of JKCS 041 is a statistical fluctuation. The
precise
value of the evidence ratio depends only slightly on the assumed prior,
provided a reasonable one is adopted. Let's assume, for example an
exponential declining prior,
,
to approximate the fact that nature usually produces a lot of
small objects (e.g. groups) per each large object
(e.g. a
rich cluster). With this prior, we found odds of 1
in 50 for
all
(N.B. smaller values of
were not considered as they give very
low probabilities for
,
which is clearly inconsistent with observations of clusters,
e.g. Abell 1958; Abell
et al. 1989).
This evidence ratio cannot be compared to p-values. They are fundamentally different quantities, and their numerical values differ by orders of magnitude (nine, in the case of JKCS 041). Evidence ratios are not commonly used in the astronomical literature to quantify the quality of a cluster detection, but we can compute them ourselves using published data for the REFLEX cluster survey (Bohringer et al. 2004). For this, we will use the property mentioned previously that the evidence ratio is the ratio of ``not-confirmed'' to ``confirmed'' clusters. Clusters in REFLEX were selected to be X-ray sources, to positionally match a galaxy overdensity, and to have a spectroscopic redshift. The XMM follow-up of a REFLEX sub-sample of clusters (REXCESS, Bohringer et al. 2007) choose 34 clusters in REFLEX not amongst the worst, and the chosen objects are said to be representative of REFLEX clusters by the authors. They found one AGN among 34 objects previously classified as clusters in REFLEX. The implied quality of a REFLEX cluster detection is therefore 1 in 33, marginally lower than the 1 in 50 computed for the JKCS 041 detection. We emphasise that the good REFLEX performance used X-ray, optical and spectroscopic information on the cluster. Instead, the ``1 in 50'' evidence ratio provided here to measure the strength of the JKCS 041 detection does not make any use of the (available) X-ray data. The JKCS 041 detection is slightly more secure than REFLEX clusters. The introduction of the X-ray evidence only strengthens the cluster detection significance, as JKCS 041 is detected as an extended X-ray source (Sect. 4). This provides a solid confirmation of our probability calculus to asses the significance of its detection.
![]() |
Figure 4:
Distribution in the generalised colour |
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3.3 SED detection using eleven bands
The second approach to detecting JKCS 041 uses all
eleven
photometric bands to identify galaxies with a spectral energy
distribution similar to a Grasil (Silva et al. 1998)
1.5 Gyr old elliptical galaxy at z=1.9
(i.e.
for the adopted cosmology).
Figures 4
is similar to 2:
it shows the colour distribution, but for a generalised
colour, ,
given by the average distance of the photometry data points from the
model SED (a 1.5 Gyr old Grasil elliptical at z=1.9).
Had we used just the J and K photometric
bands, the x-axis would be J-K
(plus an obvious warping, to bring all measurements to a common
numerical scale) and Fig. 4 would be
identical to Fig. 2,
except for the different sample selection. We consider here galaxies
with photometric data measured to better than 10% accuracy in
at
least 4 bands. In the cluster direction there is a clear
excess of
galaxies having SEDs similar to the 1.5 Gyr old Grasil
elliptical
(i.e. with small values of
). For
example, we observe 23 galaxies with
when 9.09 are expected. The probability of observing a larger
value by chance alone is 3
10-5.
At first sight, the observed
values
are large, of the order of 0.2-0.3 mag for galaxies in the
leftmost peak. This is expected, however, primarily because we are
sampling the ultraviolet (
1200-3100 Å) with
many data points.
This means that small differences between the true history of star
formation and the model result in large differences between observed
and model SEDs in this wavelength range.
This SED based approach is analogous to the photo-z approach used by Stanford et al. (2005) to detect the z=1.41 ISCS J1438+3414 cluster. Therefore, had we decided to use SED model fitting as our initial detection method, JKCS 041 would still have been detected.
The spatial distribution of these SED-selected galaxies with mag
is shown in the rightmost panel of Fig. 1.
JKCS 041 shows the largest numerical overdensity in the survey
area (53
53 arcmin2).
3.4 Cluster redshift
Due to the faintness of JKCS 041 galaxies ()
and their weak spectral features in the (observer-frame) optical, we
failed to measure spectroscopic redshifts even after an exposure of
12 h on FORS2 at VLT (progr. P277.A-5028). We
therefore
address the determination of the cluster redshift by using galaxy
colours.
The original cluster detection colour (Sect. 2),
2.1<J-K<2.5 and the
peak of the colour distribution (Sect. 3.1), both imply
either assuming a model spectral energy distribution (e.g. Bruzual
& Charlot 2003; or those
detailed in the
UKIDSS calibration paper, Hewett et al. 2006) or by
comparison with J-K colour
of
red galaxies at
(e.g. Kriek et al. 2008).
We now use our SED approach of Sect. 3.3 to compute
the
photometric cluster redshift and its uncertainty. For each redshift
value we compute the expected Grasil SED for 1.5 Gyr old
elliptical galaxy and we compute how many galaxies match (
)
this SED in the cluster direction
(a circle of 1.5 arcmin),
,
and in a reference line of sight,
,
the latter estimated in a corona centred on the cluster and with inner
and outer radii of 1.5 and 15 arcmin (and scaled by the solid
angle ratio). Assuming that these count processes are Poisson
distributed, the posterior probability
is computed assuming uniform priors, following simple algrebra (e.g.
Prosper 1989; Kraft 1991; Andreon
et al. 2006). The
posterior distribution is plotted in Fig. 5, for z>1.5
because z<1.6 is already ruled out by having
detected the cluster after filtering out
galaxies
(Sect. 3.1). The posterior probability distribution has mean
equal to z=1.98 and two peaks, one at z=1.93
and one at z=2.08. The 68% shortest
confidence interval, shaded in the figure, is
[1.84,2.12].
The length of the 68% uncertainty interval on redshift, 0.28, can also
be estimated from the error on the mean J-K colour
of the red galaxies that compose the excess in Fig. 2,
after restricting the sample to J<22.2 mag
in order to avoid upper limits. The result is 0.22 mag (the
value
is derived from the modelling described in Sect. 3.1). Since
for old galaxies at
(e.g. using models listed in Hewett et al. 2006), this
gives
(vs.
0.14=0.28/2 for half the 68%
shortest confidence interval).
![]() |
Figure 5: Photo-z redshift probability distribution, based on 11 bands photometry. The shortest 68% credible interval is marked with a (yellow) shading. |
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We emphasise that this is the statistical error. However, we
expect
a systematic error due to our use of a model SED, instead of
the
true old
galaxies SED, directly measured on the same data used by us
(i.e. UKIDSS+CFHTLS+IRAC). For example, Hewett et al.
(2006),
describing
the photometric calibration of the UKIDSS survey (from which
we took J-K), give two
predictions of the colour of an elliptical in the UKIRST photometric
system at
which differ by 0.1 mag (
). A similar
comparison (Kriek et al. 2008), based
however on real spectroscopic and J-K measurements,
displays
systematic
errors. At much lower redshift EDiSCs clusters have photo-z
systematics of
(White et al. 2005), similar
to RCS clusters prior to photometric redshift recalibration
(Gilbank et al. 2007).
This source of error has little effect on the width of the
posterior
redshift distribution: if we model this source of uncertainty
with
a top-hat filter of width ,
(i.e.
),
the 68% confidence interval of JKCS 041 redshift
is
[1.86,2.18], almost identical
to what intially derived (
[1.84,2.12]).
The redshift uncertainty has little effect on our results.
It does not change at all the probability that we detected a
cluster. Our filtering technique (Sect. 3.1) excludes a
redshift
similar to that of the ``now'' second most distant cluster of z=1.45
(Stanford et al. 2006).
It affects the value of cluster core radius (Sect. 4.1) by
less
than 1 per cent, because the angular
distance is almost
constant with redshift at .
It introduces an uncertainty on the cluster X-ray luminosity
(Sect. 4.2) by about about 15 per cent
(if
), only three times the
uncertainty implied by the uncertainty on
alone, and by a negligible quantity for the study of
scale
relation, because the mentioned 15 per cent error is
about five time smaller than the
scatter at a
given T (e.g. Stanek
et al. 2006). The
redshift uncertainty represents a minor source of error on the cluster
temperature (Sect. 4.2.).
When needed for intrinsic quantities, we adopt the lower-redshift peak of the distribution, z=1.9 as the cluster redshift, in place of the posterior average value, z=1.98, which gives conservative estimates of the cluster redshift, X-ray luminosity, and cluster mass.
![]() |
Figure 6: Number density radial profile of galaxies selected according to J-K colour ( top panel), dissimilarity from VVDS galaxies SED ( central panel), similarity to old galaxies at z=1.9 ( bottom panel). In all three cases, more galaxies in the cluster line of sight are observed than in adjacent directions. |
Open with DEXTER |
3.5 A robustness check
We now revisit our cluster detection, using a slighly more stringent magnitude limit and a simplified analysis than applied in previous Sections.
We first consider galaxies with K<20.1 (well
within the K-band limit). Those with
are brighter than the completeness limit in the J band,
J=22.2, and removed because they are not of
interest. All other K<20.1 galaxies,
regardeless of their actual detection in the J band,
will have 2.1<J-K<2.5
(we expect minimal contamination from J-K>2.5 sources).
Their number density radial profile, shown in the top panel of
Fig. 6,
indicates an excess in the inner 1 arcmin, where
9 galaxies are found when 2 are expected. This
implies a significance (p-value) of 2
10-4 (about a 3.7
detection), lower
than reported in Sect. 2, but still generally acceptable.
We then consider galaxies with K<20.5 mag.
We remove from the sample galaxies with SED matching any pair of z<1.6 VVDS galaxies
in at least three bands with good photometric quality
(<0.2 mag). Again, the radial distribution of the
remaining population (middle panel of Fig. 6) shows an
excess in the inner 1': we observe 25 galaxies
when 8 are expected.
The p-value (significance, detection likelihood)
is 10-6, a detection.
![]() |
Figure 7: z' image. Contours of equal number density of galaxies with 2.1<J-K<2.5 mag (irregular yellow contours). North is up and East is to the left. |
Open with DEXTER |
Last, we consider galaxies with K<20.5 mag,
good photometric quality (<0.2 mag) on at least
6 bands, whose SED are similar to a z=1.9 Grasil
2.5 Gyr old galaxy (Sect. 3.3). Figure 6 (bottom panel)
shows 13 galaxies in the inner 1' when 2 are
expected, giving a p-value (significance, detection
likelihood) of 2
10-7, a
detection.
Although with reduced statistical significance, due to the
reduced
sample, we confirm a spatial concentration of objects of similar colour
or SED within 1' from the cluster centre. The typical colour of the
excess population is mag,
either because this is the selecting
colour (first method), because this is the colour of the bulk of the
population after foreground removal (second method) or because it is
the colour of the template SED used to select the galaxies (third
method).
Figure 7 shows a large region around JKCS 041 in the VLT z' band, where the galaxy overdensity is highlighted by the yellow contours. A true-colour image (z'JK) of a slightly smaller region around JKCS 041 is given in Fig. 8.
3.6 A single cluster or a blend of smaller structures?
In the previous section we showed that the detection of
JKCS 041 is significant and not just a statistical
fluctuation.
However, the detection algorithms used do not provide constraints on
the
size of the detected structure along the line of sight. In particular,
they do not distinguish between the observation of a single
cluster-size object, or a projection of two (or several) groups. This
limitation is common to several other cluster detection methods. The
line of sight kernel of cluster detection by gravitational shear is
about 1000 Mpc, basically because the detected signal changes
little on these scales. Sunyaev-Zeldovich (SZ) cluster searches have an
even larger kernel, ,
basically because the signal depends on the angular size distance,
which flatens off at
.
In fact, SZ cluster surveys are likely to be confusion-limited
at masses below
(Holder et al. 2007), below this threshold cluster detections
will
frequently be blends of clusters along the line of sight. The same is
partially true at higher masses, especially considering that the
confusion error is highly non-Gaussian with a long tail to the positive
side.
![]() |
Figure 8: True colour (z'JK) image. Contours of the adaptively smoothed X-ray emission detected by Chandra (white contours). North is up and East is to the left. |
Open with DEXTER |
Red-sequence-like cluster searches have a redshift kernel that
is
given by the photometric error of the colour divided by the derivative
with redshift of the model colour. This implies
when photometry S/N is high, the Balmer break is well sampled
by
the filter pair and filters are taken near to the break (see Andreon 2003; and
Andreon et al. 2004, for an observational assessment at z<0.3
and
,
respectively).
error
at z=1.9 (which are very optimistic) would still
imply a resolution of
Mpc
along the line of sight. This is too large to discriminate, for
example, a single cluster of size of
Mpc
from two structures separated by 10 Mpc. Therefore,
red-sequence
detected clusters are also prone to confusion, as we
quantify below.
The posterior odds (probability ratio) that a detection is a
single
object or a blend of two objects each carrying about half the total
mass is given by the ratio of the probability of observing one
object,
over the probability of observing two objects,
,
in the given volume, multiplied by the relative a priori
probabilities of the two hypotheses. The latter ratio is taken to
be 1 to formalise our indifference in absence of any data.
,
with i=1,2 are assumed to be Poisson distributed.
and
are the average volume density of objects (clusters) at the redshift of
interest, assumed to be a Jenkins et al. (2001) mass
function. The volume is given by
(the 68% confidence interval) and
arcsec
(a larger separation on the sky would mean that the two
structures
could be distinguished). We also adopt a power spectrum shape parameter
and
.
To convert the mass of JKCS 041 from M500
(derived in Sect. 4.2) to the virial mass, we assume a Navarro
et al. (1997)
profile of concentration 5. We find
103.
Our result implies that, on average, one blend occurs every
4100 detections similar to JKCS 041. For three or
more
objects the odd ratio is even larger. The odds do not change
appreciably if we allow blends of similar, but not identical mass (e.g.
up to a mass ratio 1:4). Thus, there is ``decisive'' evidence
in
favour of a single object, when the evidence is measured on the
Jeffreys (1961)
probability scale. We emphasise once more that quoted probabilities are
not p-values, and that probabilities
(quoted here) cannot be compared to p-values
(often quoted in other studies).
Continuing our survey on cluster detection methods, the size
of a
structure can obviously also be assessed with spectroscopic data,
because even a few galaxies at small
(of the order of 1000 km s-1)
are sufficient to establish with reasonable confidence that most of a
given structure is of cluster size. Of course, one should also account
for the non negligible possibility that a number of concordant
redshifts are found by chance (see e.g. Gal et al. 2008). For
example, in the JKCS 041 direction, we found
9 galaxies with redshift
within 2 arcmin from the cluster centre. However, the same
number
of concordant redshifts is found in almost every other region of the
VVDS area (
40 arcmin)
that had similar sampling rate (the redshift survey is not spatially
uniform). This is just one of the redshift spikes in the general
JKCS 041 area (Lefevre et al. 2005), and is not the
redshift
of every cluster in the area.
![]() |
Figure 9: A
background
subtracted, exposure corrected [0.3-2] keV Chandra X-ray image
of
JKCS 041, binned to 4 arcsec pixels. The image is
overlaid
with contours of the X-ray emission after adaptive smoothing so that
all features are significant to at least the 3 |
Open with DEXTER |
4 Chandra X-ray observations
JKCS041 was observed by Chandra for 75 ks on
2007 November 23 (ObsID 9368), using the
ACIS-S detector. The data were reduced using the standard data
reduction procedures as outlined in Maughan et al. (2008).
A preliminary examination of the cluster spectrum showed that
the
0.3-2.0 keV energy band gave the maximum cluster signal to
noise
ratio for our image
analysis. An image was produced in this energy band, and is
shown
in Fig. 9.
The
image was then adaptively smoothed so all features were significant to
at least ,
using a version of the Ebeling et al. (2006)
algorithm modified to include exposure correction. Contours of this
smoothed X-ray image are overlaid in Fig. 9 and on the
true-colour image in Fig. 8.
The X-ray morphology appears regular, but this should be interpreted
with caution due to the relatively large smoothing kernel required by
the low signal to noise cluster emission. The
of this Gaussian kernel was
arcsec
within a radius of 30 arcsec of the cluster centre. Within
a 1 arcmin radius from the cluster centre there are
223
31 photons in the 0.3-2 keV band (after subtraction
of the background and exclusion of point
sources).
4.1 X-ray image analysis
The Chandra image of JKCS041 was fit in Sherpa with
a two-dimensional (2D) model chosen to describe the cluster surface
brightness distribution. The model used was the Sherpa
implimentation of the 2D -profile
with an additive constant background component. The model was
constrained to be circular and was fit to the data using the Cash (1979)
statistic, appropriate for low numbers of counts per bin, including
convolution with a model of the Chandra
PSF and multiplication by the appropriate exposure map. Point sources
were masked out of the data and model during the fitting process. The
point source 8 arcsec to the south east of the cluster X-ray
centre is relatively bright (and already detected in Chiappetti
et al. 2005),
contributing
photons
or
of the total cluster and point source X-ray flux in the imaging band
(the other point sources are much fainter). Fortunately, this emission
is
easily resolved by Chandra for exclusion from our analysis.
![]() |
Figure 10: Radial profiles of JKCS 041, and the best fitting 2D model. |
Open with DEXTER |
An advantage of the 2D fitting used here compared with fitting
a
model to a one-dimensional (1D) surface brightness profile is
that
the centre of such a 1D profile is subject to
significant
uncertainties in low signal to noise data like these. Changes in the
central position of the 1D profile have a strong effect on the
profile's shape. In a 2D model, those uncertainties
can be
explicitly included by allowing the centre coordinates of the
2D model to be free parameters. The additional free parameters
in
our fit were the core radius
of the model, and the source and background normalisations. The
slope
of the surface brightness model could not be constrained by the data
and was fixed at
.
The best fitting model to JKCS 041 had central coordinates of
6 arcsec and
4 arcsec, with a core radius of
36.6+8.3-7.6 arcsec
(
307+70-64 kpc).
Figure 10
shows a radial profile of the data and the best fitting
2D model.
Note that this is simply for visualisation purposes, the model was not
fit in this
space. This figure demonstrates convincingly that the
arcsec
X-ray emission is extended with respect to the 0.5 arcsec
Chandra
PSF. The cluster core radius, about 300 kpc, is in the range
of
values observed for local clusters.
4.2 X-ray spectral analysis
Our spectral analysis procedure was chosen to match that of Pacaud
et al. (2007) to allow
direct comparison with their relation,
presented in Andreon et al. (in preparation). In
summary, a
cluster spectrum was extracted from an aperture of radius
40 arcsec (336 kpc) (with point sources excluded),
chosen to
maximise the signal to noise ratio. A background spectrum was
extracted from an annular region around the cluster, sufficiently
separated to exclude any cluster emission (inner and outer radii: 99''
and 198''; 833 and 1665 kpc). The resulting cluster
spectrum
contained approximately 210 source photons in the
0.3-7.0 keV
band used for spectral
fitting, with a signal to noise ratio of 7.4. The source
spectrum
was fit with an absorbed APEC (Smith et al. 2001) plasma
model, with the absorbing column fixed at the Galactic value
(2.61
1020 cm-2),
the metal abundance relative to Solar fixed at 0.3 and the
redshift of the plasma model fixed at 1.9. The spectrum was
grouped to contain a minimum of
5 counts per bin and the
model was fit to the background-subtracted data using the XSPEC
C-statistic.
The best fitting spectral model had a temperature of
7.6+5.3-3.3 keV
(plotted in Fig. 11)
and gave an unabsorbed bolometric X-ray flux of 1.93
10-14 erg cm-2 s-1
(corrected for area lost to point sources in the spectral aperture).
This temperature was used to estimate R500=0.52 Mpc
(the radius within which the mean
density is 500 times the critical density at the cluster
redshift), using the scaling relation of Finoguenov et al. (2001) as given
in Pacaud et al. (2007;
their Eq. (2)). The best fitting 2D surface
brightness model
was then used to scale the observed flux from the spectral aperture to
this radius, including correction for point sources. The bolometric
luminosity within R500
was thus found to be
1044 erg s-1,
with
these errors including the uncertainties on the normalisation of the
spectral model, on its temperature and on the core radius of the
2D model used for the aperture correction. This is consistent
again with Pacaud et al. (2007) with the
exception that we do not include the
uncertainties on the surface brightness model slope, which is
unconstrained by our data.
![]() |
Figure 11: The Chandra X-ray spectrum of JKCS 041 and the best fitting model are shown in the top panel, with the residuals from the model shown in the bottom panel. The spectrum is binned for displaying purposes. |
Open with DEXTER |
Finally, the temperature of JKCS 041 can be used to
estimate
the cluster's mass. For consistency, we can simply use the definition
of R500 given above to yield
,
under the (strong) assumption that the Finoguenov et al. (2001) relation
holds at z=1.9.
4.3 Testing a non-thermal origin for the X-ray emission
Fabian et al. (2001, 2003) interpreted the extended X-ray source coincident with the powerful radio source 3C 294 at z = 1.768 as due to CMB photons scattered at high energy through inverse Compton scattering on non-thermal electrons produced by this radio source. This is the only known case of such a phenomenum. In the case of JKCS 041, the X-ray spectrum alone is unable to rule out a non-thermal emission model, but the latter is an unlikely interpretation for several reasons. Firstly, there is no significant radio source present in the cluster core. Bondi et al. (2003, 2007) present VLA and GMRT radio observations of this field as part of the VVDS-VLA field. The three sources detected close to JKCS 041 are marked in Fig. 9. None of the radio sources appear associated with the extended X-ray emission, although the eastern-most source is associated with a faint X-ray point source with an optical counterpart
Furthermore, the morhopology of the X-ray emission in
3C 294
was found to be hourglass shaped, and contained within a radius of
100 kpc. Within the limits of the Chandra data for
JKCS 041,
there is no evidence for irregular X-ray morphology, and the
X-ray
emission is detected to a radius of kpc (see
Fig. 10).
![]() |
Figure 12: Temperature likelihood of JKCS 041. |
Open with DEXTER |
4.4 Is JKCS 041 a filament?
We now consider the possibility that JKCS 041 is a filament viewed along the line of sight. In short, the X-ray data rule out this possibility on two fronts: the gas is too dense and too hot.
The density implied by the X-ray emission (as determined from
the
normalisation of the best fitting spectral model) if the emission were
due to a cylindrical filament of uniform density gas of
length 10 Mpc along the line of sight and radius
0.326 Mpc
(our spectral aperture) is
0.4
10-4 cm-3
(giving a mass density of 1.0
0.1
10-27 g cm-3).
This is significantly higher than the gas densities found in large
scale filaments in e.g. the simulations of Dolag et al. (2006), who
found densities typically much lower than 10-28 g cm-3.
Second, the measured temperature of the gas implies a deep
gravitational potential well, and is inconsistent with the
<1 keV temperatures predicted in such filamentary
structures
(e.g. Pierre
et al. 2000; Dolag
et al. 2006).
To test how strongly the data can rule out
<1 keV gas, we need to calculate the ratio of the
probability that T<1, as expected
for filaments, and the probability that T>1,
as usual for clusters. The temperature likelihood (the output of
STEPPAR in Xspec), used for the computation, is depicted in
Fig. 12.
If we adopt an uniform prior in T, to
express our indifference on T prior
to observing any data, over a range abundantly encompassing all
reasonable temperatures (say, from 0.03 to 20 keV), we found
odds
of 1 in .
This demonstrates that the filament hypothesis is rejected by the
observed X-ray spectrum. Figure 12
shows that this very small probability ratio is insensitive the precise
value (e.g. 2 keV vs. 1 keV) of the
temperature
threshold adopted to define the two hypotheses.
5 Discussion
5.1 The redshift of the X-ray emission
The redshift of the X-ray emitting gas, unless it is directly derived from the X-ray spectrum, is commonly assumed to be the same of the galaxy overdensity spatially coincident with it. This is the tacit assumption made in all but a couple of studies related to cluster gas intrinsic properties, like X-ray luminosity or temperature. We make the same assumption and we further check that no other cluster is in the JKCS 041 line of sight.
JKCS 041 lies in a region rich of photometric data,
which have
been exploited by several groups
independently. Using CFHTLS data, Olsen et al.
(2007), Grove
et al. (2009) and Mazure et al. (2007) explored the
range up
to
using several filters and detection methods. None of their candidate
clusters spatially matches JKCS 041, the nearest being
6.5 arcmin away. We also found no matching detection by using R
and z' bands and the very same algorithm
used with J and K.
Therefore, we found no evidence of another cluster on the
JKCS 041 line of sight.
5.2 JKCS 041 and cosmological parameters
The detection of JCSK 041 is in line with the expectation of a
CDM model,
with
and power spectral shape parameter
and a Jenkins et al. (2001)
mass function. In the surveyed area (about 53
53 arcmin2) and within 1.8<z<2.0
about 2.4 clusters with M>1013.75 h-1
are expected.
In the past, cosmological parameters have been constrained by the observation of a single high redshift cluster. The argument used is quite simple: cosmological parameters that predict in the studied volume fewer than one cluster more massive than the observed cluster are disregarded in favour of those that make predictions close to the observed number of clusters (i.e. one). Because the first discovered high redshift object is usually extreme (being extreme makes its discovery easier), it is likely to fall in the tail of the distribution. Trying to invert the argument, and constraining cosmological parameter values after having observed a likely extreme object is quite dangerous. This assumes a perfect knowledge of the tail of the distribution from which the object is drawn, which is seldom true on general grounds, and it is certainly not true in this specific case. The halo mass function differs somewhat at the high mass end between different theoretical determinations, e.g. Jenkins et al. (2001), Press & Schechter (1974), Sheth & Tormen (1999) and numerical simulations (see Jenkins et al. 2001).
For this reason, we do not attempt to constrain cosmological parameters from the JKCS 041 discovery, and we simply note that its detection is in line with (model dependent) predictions.
6 Summary
We report the discovery of a massive near-infrared selected cluster of
galaxy at .
The evidence relies both on eleven band optical, near-infrared and
Spitzer photometry, and on the detection of extended X-ray emission in
Chandra data. The estimate of the redshift is based on the observed
galaxy colours and fitting with SED of old galaxies.
The cluster is centred at
and
,
and has a bolometric X-ray luminosity within R500
of
1044 erg s-1.
Spatial and spectral analysis indicate an X-ray core radius of
36.6+8.3-7.6 arcsec
(about 300 kpc), an X-ray temperature of
7.6+5.3-3.3 keV,
and a mass of M500=2.9+3.8-2.4
,
the latter derived under the usual (and strong) assumptions.
The cluster is originally discovered using a modified red-sequence method based on near-infrared photometry, and is subsequently detected both by removing galaxies with SEDs similar to any two of the 104 VVDS galaxies with z<1.6 in the region, and by using a SED fitting technique to isolate z=1.9 Grasil ellipticals. By means of the latter we find the cluster redshift to be 1.84<z<2.12 at 68% confidence. The X-ray detection from follow-up Chandra observations, together with statistical arguments, discard the hypothesis of a blend of groups or a filament along the line of sight. The absence of a strong radio source makes scattering of CMB photons at X-ray energies also an unlikely explanation for the X-ray emission.
Therefore, we conclude that JKCS 041 is a cluster of galaxies
at
with a deep potential well, making it the highest redshift
cluster currently known, with extended X-ray emission.
X-ray scaling relations of JKCS 041, and other clusters at lower redshift, will be discussed in Andreon et al. (in preparation). Sunyaev-Zeldovich observations of JKCS 041 are in progress at the SZ array.
AcknowledgementsWe would like to thank the referee, Harald Ebeling, for a detailed report which allowed us to improve our paper. S.A. thanks Marcella Longhetti and Emanuela Pompei for useful discussions. Most of the Bayesian analysis in this paper benefitted from discussions (mostly by e-mail) with Giulio D'Agostini, Steve Gull, Merrilee Hurn and Roberto Trotta. This paper is based on observations obtained by UKIDSS (see standard acknowledgement at the URL http://www.ukidss.org/archive/archive.html) Chandra (ObsID 9368) and ESO (277.A-5028). B.J.M. was partially supported during this work by NASA through Chandra guest observer grant GO8-9117X. J.K. acknowledges financial support from Deutsche Forschungsgemeinschaft (DFG) grant SFB 439.
References
- Abell, G. O. 1958, ApJS, 3, 211 [CrossRef] [NASA ADS]
- Abell, G. O., Corwin, H. G., Jr., & Olowin, R. P. 1989, ApJS, 70, 1 [CrossRef] [NASA ADS]
- Andreon, S. 2003, A&A, 409, 37 [EDP Sciences] [CrossRef] [NASA ADS]
- Andreon, S. 2006a, A&A, 448, 447 [EDP Sciences] [CrossRef] [NASA ADS]
- Andreon, S. 2006b, MNRAS, 369, 969 [CrossRef] [NASA ADS]
- Andreon, S. 2008, MNRAS, 386, 1045 [CrossRef] [NASA ADS]
- Andreon, S. 2009, in Bayesian Methods in Cosmology, ed. M. P. Hobson, et al. (Cambridge, UK: Cambridge University Press), ISBN-13: 9780521887946
- Andreon, S., Willis, J., Quintana, H., et al. 2004a, MNRAS, 353, 353 [CrossRef] [NASA ADS]
- Andreon, S., Willis, J., Quintana, H., et al. 2004b, in Exploring the Universe. Contents and Structure of the Universe, XXXIX Rencontres de Moriond [arXiv:astro-ph/0405574]
- Andreon, S., Valtchanov, I., Jones, L. R., et al. 2005, MNRAS, 359, 1250 [CrossRef] [NASA ADS]
- Andreon, S., Quintana, H., Tajer, M., Galaz, G., & Surdej, J. 2006, MNRAS, 365, 915 [CrossRef] [NASA ADS]
- Andreon, S., Puddu, E., de Propris, R., & Cuillandre, J.-C. 2008, MNRAS, 385, 979 [CrossRef] [NASA ADS]
- Bondi, M., Ciliegi, P., Zamorani, G., et al. 2003, A&A, 403, 857 [EDP Sciences] [CrossRef] [NASA ADS]
- Bondi, M., Ciliegi, P., Venturi, T., et al. 2007, A&A, 463, 519 [EDP Sciences] [CrossRef] [NASA ADS]
- Böhringer, H., Schuecker, P., Guzzo, L., et al. 2004, A&A, 425, 367 [EDP Sciences] [CrossRef] [NASA ADS]
- Böhringer, H., Schuecker, P., Pratt, G. W., et al. 2007, A&A, 469, 363 [EDP Sciences] [CrossRef] [NASA ADS]
- Bremer, M. N., Valtchanov, I., Willis, J., et al. 2006, MNRAS, 371, 1427 [CrossRef] [NASA ADS]
- Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [CrossRef] [NASA ADS]
- Cash, W. 1979, ApJ, 228, 939 [CrossRef] [NASA ADS]
- Chiappetti, L., Tajer, M., Trinchieri, G., et al. 2005, A&A, 439, 413 [EDP Sciences] [CrossRef] [NASA ADS]
- Crawford, S. M., Bershady, M. A, & Hoessel, J. G. 2009, ApJ, 690, 1158 [CrossRef] [NASA ADS]
- de Propris, R., Stanford, S. A., Eisenhardt, P. R., Dickinson, M., & Elston, R. 1999, AJ, 118, 719 [CrossRef] [NASA ADS]
- Dolag, K., Meneghetti, M., Moscardini, L., Rasia, E., & Bonaldi, A. 2006, MNRAS, 370, 656 [NASA ADS]
- Dye, S., Warren, S. J., Hambly, N. C., et al. 2006, MNRAS, 372, 1227 [CrossRef] [NASA ADS]
- Ebeling, H., White, D. A., & Rangarajan, F. V. N. 2006, MNRAS, 368, 65 [NASA ADS]
- Ebeling, H., Barrett, E., Donovan, D., et al. 2007, ApJ, 661, L33 [CrossRef] [NASA ADS]
- Fabian, A. C., Crawford, C. S., Ettori, S., & Sanders, J. S. 2001, MNRAS, 322, L11 [CrossRef] [NASA ADS]
- Fabian, A. C., Sanders, J. S., Crawford, C. S., & Ettori, S. 2003, MNRAS, 341, 729 [CrossRef] [NASA ADS]
- Feroz, F., & Hobson, M. P. 2008, MNRAS, 384, 449 [CrossRef] [NASA ADS]
- Finoguenov, A., Reiprich, T. H., & Bohringer, H. 2001, A&A, 368, 749 [EDP Sciences] [CrossRef] [NASA ADS]
- Franx, M., Labbé, I., Rudnick, G., et al. 2003, ApJ, 587, L79 [CrossRef] [NASA ADS]
- Gal, R. R., Lemaux, B. C., Lubin, L. M., Kocevski, D., & Squires, G. K. 2008, ApJ, 684, 933 [CrossRef] [NASA ADS]
- Gilbank, D. G., & Balogh, M. L. 2008, MNRAS, 385, L116 [NASA ADS]
- Gilbank, D. G., Yee, H. K. C., Ellingson, E., et al. 2007, AJ, 134, 282 [CrossRef] [NASA ADS]
- Gladders, M. D., & Yee, H. K. C. 2000, AJ, 120, 2148 [CrossRef] [NASA ADS]
- Gladders, M. D., & Yee, H. K. C. 2005, ApJS, 157, 1 [CrossRef] [NASA ADS]
- Goto, T., Sekiguchi, M., Nichol, R. C., et al. 2002, AJ, 123, 1807 [CrossRef] [NASA ADS]
- Jeffreys, H. 1961, Theory of probability, 3rd edn. (Oxford: Oxford Univ. Press)
- Jenkins, A., Frenk, C. S., White, S. D. M., et al. 2001, MNRAS, 321, 372 [CrossRef] [NASA ADS]
- Hewett, P. C., Warren, S. J., Leggett, S. K., & Hodgkin, S. T. 2006, MNRAS, 367, 454 [CrossRef] [NASA ADS]
- Kodama, T., Arimoto, N., Barger, A. J., & Arag'on-Salamanca, A. 1998, A&A, 334, 99 [NASA ADS]
- Koester, B. P., McKay, T. A., Annis, J., et al. 2007, ApJ, 660, 239 [CrossRef] [NASA ADS]
- Kraft, R. P., Burrows, D. N., & Nousek, J. A. 1991, ApJ, 374, 344 [CrossRef] [NASA ADS]
- Kriek, M., van Dokkum, P. G., Franx, M., et al. 2008, ApJ, 677, 219 [CrossRef] [NASA ADS]
- Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841 [EDP Sciences] [CrossRef] [NASA ADS]
- Lawrence, A., Warren, S. J., Almaini, O., et al. 2007, MNRAS, 379, 1599 [CrossRef] [NASA ADS]
- Le Fèvre, O., Vettolani, G., Garilli, B., et al. 2005, A&A, 439, 845 [EDP Sciences] [CrossRef] [NASA ADS]
- Liddle, A. R. 2004, MNRAS, 351, L49 [CrossRef] [NASA ADS]
- Lidman, C., Rosati, P., Tanaka, M., et al. 2008, A&A, 489, 981 [EDP Sciences] [CrossRef] [NASA ADS]
- Lonsdale, C. J., Smith, H. E., Rowan-Robinson, M., et al. 2003, PASP, 115, 897 [CrossRef] [NASA ADS]
- Maughan, B. J., Jones, C., Forman, W., & Van Speybroeck, L. 2008, ApJS, 174, 117 [CrossRef] [NASA ADS]
- Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller A., & Teller, E. 1953, J. Chem. Phys, 21, 1087 [CrossRef] [NASA ADS]
- Mullis, C. R., Rosati, P., Lamer, G., et al. 2005, ApJ, 623, L85 [CrossRef] [NASA ADS]
- Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493 [CrossRef] [NASA ADS]
- Pacaud, F., Pierre, M., Adami, C., et al. 2007, MNRAS, 382, 1289 [NASA ADS]
- Papovich, C., Moustakas, L. A., Dickinson, M., et al. 2006, ApJ, 640, 92 [CrossRef] [NASA ADS]
- Pierre, M., Bryan, G., & Gastaud, R. 2000, A&A, 356, 403 [NASA ADS]
- Pierre, M., Valtchanov, I., Altieri, B., et al. 2004, J. Cosmol. Astro-Part. Phys., 9, 11 [CrossRef] [NASA ADS]
- Press, W. H., & Schechter, P. 1974, ApJ, 187, 425 [CrossRef] [NASA ADS]
- Prosper, H. 1998, Phys. Rev. D, 37, 1153 [CrossRef] [NASA ADS]
- Reddy, N. A., Erb, D. K., Steidel, C. C., et al. 2005, ApJ, 633, 748 [CrossRef] [NASA ADS]
- Regnault, N., Conley, A., Guy, J., et al. 2009, A&A, 506, 999 [EDP Sciences] [CrossRef]
- Saracco, P., Giallongo, E., Cristiani, S., et al. 2001, A&A, 375, 1 [EDP Sciences] [CrossRef] [NASA ADS]
- Schirmer, M., Erben, T., Hetterscheidt, M., & Schneider, P. 2007, A&A, 462, 875 [EDP Sciences] [CrossRef] [NASA ADS]
- Silva, L., Granato, G. L., Bressan, A., & Danese, L. 1998, ApJ, 509, 103 [CrossRef] [NASA ADS]
- Surace, J., et al. 2005, see: http://swire.ipac.caltech.edu/swire/astronomers/data_access.html
- Smith, R. K., Brickhouse, N. S., Liedahl, D. A., & Raymond, J. C. 2001, ApJ, 556, L91 [CrossRef] [NASA ADS]
- Stanek, R., Evrard, A. E., Böhringer, H., Schuecker, P., & Nord, B. 2006, ApJ, 648, 956 [CrossRef] [NASA ADS]
- Stanford, S. A., Eisenhardt, P. R., & Dickinson, M. 1998, ApJ, 492, 461 [CrossRef] [NASA ADS]
- Stanford, S. A., Eisenhardt, P. R., Brodwin, M., et al. 2005, ApJ, 634, L129 [CrossRef] [NASA ADS]
- Stanford, S. A., Romer, A. K., Sabirli, K., et al. 2006, ApJ, 646, L13 [CrossRef] [NASA ADS]
- Staniszewski, Z., Ade, P. A. R., Aird, K. A., et al. 2009, ApJ, 701, 32 [CrossRef] [NASA ADS]
- Sheth, R. K., & Tormen, G. 1999, MNRAS, 308, 119 [CrossRef] [NASA ADS]
- Stott, J. P., Smail, I., Edge, A. C., et al. 2007, ApJ, 661, 95 [CrossRef] [NASA ADS]
- Tanaka, M., Finoguenov, A., Kodama, T., et al. 2008, A&A, 489, 571 [EDP Sciences] [CrossRef] [NASA ADS]
- Trotta, R. 2007, MNRAS, 378, 72 [CrossRef] [NASA ADS]
- White, S. D. M., Clowe, D. I., Simard, L., et al. 2005, A&A, 444, 365 [EDP Sciences] [CrossRef] [NASA ADS]
- Wilks, S. 1938, Ann. Math. Stat., 9, 60 [CrossRef]
- Wittman, D., Tyson, J. A., Margoniner, V. E., Cohen, J. G., & Dell'Antonio, I. P. 2001, ApJ, 557, L89 [CrossRef] [NASA ADS]
- Wittman, D., Dell'Antonio, I. P., Hughes, J. P., et al. 2006, ApJ, 643, 128 [CrossRef] [NASA ADS]
Footnotes
- ... JKCS 041
- Cluster names are acronyms indicating the filter pair used for the detection ( gr, Rz, JK) followed by the string CS, for colour selected, followed by the order number in the catalogue. These names are IAU-compliant as the acronyms are registered.
- ... each
- The likelihood ratio theorem, or statistical tests build on it, such as the F-test, cannot be used in our case because the tested model is on the boundary of the parameter space, see e.g. Protassov et al. (2001) or Andreon (2009) for an astronomical introduction.
All Figures
![]() |
Figure 1:
Number density image of a region of 146 Mpc2
(0.16 deg2) area centred on
JKCS 041. In the left panel, only
2.1<J-K<2.5 mag
galaxies are considered, and all the ancillary photometry ignored. This
shows the original cluster detection. In the central panel,
we
have discarded foreground galaxies, as identified by their spectral
energy distribution (SED), using spectroscopy, optical and
near-infrared photometry. In the right panel, we
only keep galaxies with SEDs similar to the Grasil 1.5 Gyr old
ellipticals at z=1.9
and we also use Spitzer
photometry. JKCS 041, at the centre of each panel, is clearly
detected in all images, independently of the filtering applied. The
images have been smoothed with a Gaussian with |
Open with DEXTER | |
In the text |
![]() |
Figure 2:
The J-K
colour distribution of galaxies within 1.5 arcmin
(750 kpc)
of the cluster centre is shown by the solid histogram. The same
distribution measured in the control field (a 1.5<r<15 arcmin
annulus) and normalised to the cluster area is shown by the dashed
histogram with error bars. Foregound galaxies were removed in both
cases. A clear excess is seen at
|
Open with DEXTER | |
In the text |
![]() |
Figure 3: The
black jagged
curve shows the posterior probability of the number of
JKCS 041
members. Also plotted is a Gaussian with the same mean and standard
deviation (red curve). Note that the Gaussian is a poor approximation
to the probability distribution at low
|
Open with DEXTER | |
In the text |
![]() |
Figure 4:
Distribution in the generalised colour |
Open with DEXTER | |
In the text |
![]() |
Figure 5: Photo-z redshift probability distribution, based on 11 bands photometry. The shortest 68% credible interval is marked with a (yellow) shading. |
Open with DEXTER | |
In the text |
![]() |
Figure 6: Number density radial profile of galaxies selected according to J-K colour ( top panel), dissimilarity from VVDS galaxies SED ( central panel), similarity to old galaxies at z=1.9 ( bottom panel). In all three cases, more galaxies in the cluster line of sight are observed than in adjacent directions. |
Open with DEXTER | |
In the text |
![]() |
Figure 7: z' image. Contours of equal number density of galaxies with 2.1<J-K<2.5 mag (irregular yellow contours). North is up and East is to the left. |
Open with DEXTER | |
In the text |
![]() |
Figure 8: True colour (z'JK) image. Contours of the adaptively smoothed X-ray emission detected by Chandra (white contours). North is up and East is to the left. |
Open with DEXTER | |
In the text |
![]() |
Figure 9: A
background
subtracted, exposure corrected [0.3-2] keV Chandra X-ray image
of
JKCS 041, binned to 4 arcsec pixels. The image is
overlaid
with contours of the X-ray emission after adaptive smoothing so that
all features are significant to at least the 3 |
Open with DEXTER | |
In the text |
![]() |
Figure 10: Radial profiles of JKCS 041, and the best fitting 2D model. |
Open with DEXTER | |
In the text |
![]() |
Figure 11: The Chandra X-ray spectrum of JKCS 041 and the best fitting model are shown in the top panel, with the residuals from the model shown in the bottom panel. The spectrum is binned for displaying purposes. |
Open with DEXTER | |
In the text |
![]() |
Figure 12: Temperature likelihood of JKCS 041. |
Open with DEXTER | |
In the text |
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