The XMM-BCS galaxy cluster survey: I. The X-ray selected cluster catalog from the initial 6 deg$^2$

Abstract

The XMM-Newton - Blanco Cosmology Survey project (XMM-BCS) is a coordinated
X-ray, optical and mid-infrared cluster survey in a field also covered by
Sunyaev-Zel'dovich effect surveys by the South Pole Telescope and the Atacama
Cosmology Telescope. The aim of the project is to study the cluster population
in a 14 deg$^2$ field.
In this work, we present a catalog of 46 X-ray selected clusters from the
initial 6 deg$^2$ survey core. We describe the XMM-BCS source detection
pipeline and derive physical properties of the clusters. We provide photometric
redshift estimates derived from the BCS imaging data and spectroscopic redshift
measurements for a low redshift subset of the clusters. We derive the cluster
log N - log S relation using an approximation to the survey selection function
and find it in good agreement with previous studies. We carry out an initial
comparison between X-ray luminosity derived masses and masses from optical
estimators from the Southern Cosmology Survey for a subset of the cluster
sample. Optical masses based on cluster richness and total optical luminosity
are found to be significantly higher than the X-ray values.
(abridged)

Full text

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arXiv:1111.0141v1 [astro-ph.CO] 1 Nov 2011
Astronomy & Astrophysics manuscript no. xmmbcs
November 2, 2011
c ? ESO 2011
The XMM-BCS galaxy cluster survey
I. The X-ray selected cluster catalog from the initial 6 deg2
R.ˇSuhada1,2⋆, J. Song3, H. B¨ ohringer1, J. J. Mohr1,2,4, G. Chon1, A. Finoguenov1,5, R. Fassbender1,5, S. Desai2,6,
R. Armstrong7, A. Zenteno2,4, W. A. Barkhouse8, E. Bertin9, E. J. Buckley-Geer10, S. M. Hansen11, F. W. High12,
H. Lin10, M. M¨ uhlegger1, C. C. Ngeow13, D. Pierini⋆⋆, G. W. Pratt14, M. Verdugo1, and D. L. Tucker10
1Max-Planck-Institut f¨ ur extraterrestrische Physik, Giessenbachstr. 1, 85748 Garching, Germany
2Department of Physics, Ludwig-Maximilians-Universit¨ at, Scheinerstr. 1, 81679 Munich, Germany
3University of Michigan, Physics Department, 450 Church Street, Ann Arbor, MI 48109-1040, USA
4Excellence Cluster Universe, Boltzmannstr. 2, 85748 Garching, Germany
5University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
6Department of Astronomy, 1002 W. Green Street, Urbana, IL 61801, USA
7National Center for Supercomputing Applications, University of Illinois, 1205 West Clark Street, Urbana, IL 61801, USA
8Department of Physics & Astrophysics, University of North Dakota, Grand Forks, ND 58202
9Institut d’Astrophysique de Paris, UMR 7095 CNRS, Universit´ e Pierre et Marie Curie, 98 bis boulevard Arago, F-75014 Paris,
France
10Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510
11University of California Observatories & Department of Astronomy, University of California, Santa Cruz, CA 95064
12University of Chicago, 5640 South Ellis Avenue, Chicago, IL 6063
13Graduate Institute of Astronomy, National Central University, No. 300 Jonghda Rd, Jhongli City 32001, Taiwan
14Laboratoire AIM, IRFU/Service d’Astrophysique - CEA/DSM - CNRS - Universit´ e Paris Diderot, Bˆ at. 709, CEA-Saclay, F-91191
Gif-sur-Yvette Cedex, France
Received/accepted
ABSTRACT
TheXMM-Newton-BlancoCosmology Surveyproject (XMM-BCS)isacoordinated X-ray, optical andmid-infraredcluster surveyin
a field also covered by Sunyaev-Zel’dovich effect (SZE) surveys by the South Pole Telescope and the Atacama Cosmology Telescope.
The aim of the project is to study the cluster population in a 14 deg2field (center: α ≈ 23:29:18.4, δ ≈ -54:40:33.6). The uniform
multi-wavelength coverage will also allow us for the first time to comprehensively compare the selection function of the different
cluster detection approaches in a single test field and perform a cross-calibration of cluster scaling relations.
In this work, we present a catalog of 46 X-ray selected clusters from the initial 6 deg2survey core. We describe the XMM-BCS source
detection pipeline and derive physical properties of the clusters. We provide photometric redshift estimates derived from the BCS
imaging data and spectroscopic redshift measurements for a low redshift subset of the clusters. The photometric redshift estimates are
found to be unbiased and in good agreement with the spectroscopic values.
Our multi-wavelength approach gives us a comprehensive look at the cluster and group population up to redshifts z ≈ 1. The median
redshift of the sample is0.47 and the median mass M500≈ 1×1014M⊙(∼ 2 keV). From the sample, we derive the cluster logN−logS
using an approximation to the survey selection function and find it in good agreement with previous studies.
We compare optical mass estimates from the Southern Cosmology Survey available for part of our cluster sample with our estimates
derived from the X-ray luminosity. Weak lensing masses available for a subset of the cluster sample are in agreement with our
estimates. Optical masses based on cluster richness and total optical luminosity are found to be significantly higher than the X-ray
values.
The present results illustrate the excellent potential of medium-deep, X-ray surveys to deliver cluster samples for cosmological
modelling. In combination with available multi-wavelength data in optical, near-infrared and SZE, this will allow us to probe the
dependence of the selection functions on relevant cluster observables and provide thus an important input for upcoming large-area
multi-wavelength cluster surveys.
Key words. Surveys, Catalogs, Galaxies: clusters: general, Cosmology: Large-scale structure of Universe
1. Introduction
The formation of the cold dark matter (CDM) dominated large-
scale structure of the Universe is hierarchical with smallest ob-
jects collapsing first. With passing time more and more massive
structures are able to decouple from the Hubble flow and en-
⋆email: rsuhada@usm.lmu.de
⋆⋆Visiting astronomer at MPE.
ter the non-linear regime, collapse and eventually virialize. The
statistical properties of the matter density field (e.g. its power
spectrum)as well as the growthof the structures are stronglyde-
pendent on the background cosmology and can be thus used to
put constraints on cosmological models.
From this point of view, clusters occupy a very important
place in the structure formation scenario, by being the most re-
cent (i.e. redshifts z ? 2 - coincident with the onset of the dark
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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
energy dominance) and thus also the most massive structures
(1013−1015M⊙) to virialize. The cluster abundance is therefore
exponentially sensitive to the growth of the large scale-structure
and to the underlying cosmological parameters (Haiman et al.
2001; Majumdar & Mohr 2003; Haiman et al. 2005).
The key parameter in cosmological tests of this type - the
total mass of clusters (identified with dark matter halos) - is it-
self not directly observable. Fortunately, in first approximation,
clusters are virialized and their growth is gravitationally driven
and therefore self-similar. This allows us to link their mass to
some suitable observable quantity originating from the baryonic
components of a cluster - its galaxy population and the intra-
clustermedium(ICM).TheICM is directlyobservableinX-rays
or through the distortion of the Cosmic Microwave Background
(CMB) imprinted by the ICM thermal electron population via
inverse Compton scattering (the so-called Sunyaev-Zel’dovich
effect (SZE), Sunyaev & Zel’dovich 1972).
Since the ICM closely traces the DM potential, it offers
better (i.e. lower scatter) mass-proxies than those available
from optical observations of the cluster’s galaxy population
(e.g. Reyes et al. 2008). In X-rays, the simplest and observa-
tionally least expensive mass-proxy is the X-ray luminosity
LX (Reiprich & B¨ ohringer 2002; Pratt et al. 2009; Mantz et al.
2010a).
For the SZE experiments the most direct way to esti-
mate the cluster mass is from the source signal-to-noise ra-
tio (e.g. Williamson et al. 2011; Vanderlinde et al. 2010) and
more importantly, through the integrated Compton parameter
YSZ. Numerical simulations suggest that YSZ is an excellent
proxy of cluster mass (da Silva et al. 2004; Motl et al. 2005;
Nagai 2006). First cross-comparisons with X-ray and SZE stud-
ies are generally finding good agreement between the mass
estimates and no significant deviation from the self-similar
predictions (Planck Collaboration 2011c,b,a; Melin et al. 2011;
Andersson et al. 2010; Marrone et al. 2009; Bonamente et al.
2008).
If deeper X-ray observations are available, we can use the
spectroscopic temperature TX, gas mass Mg and their combi-
nation YX = TXMg(the X-ray analogue to the YSZparameter,
Kravtsov et al. 2006; Vikhlinin et al. 2009; Arnaud et al. 2010)
as good mass proxies. Using the YXparameter Vikhlinin et al.
(2009) put a strong constraint on the cosmological parameters
includingthe dark energyequation of state. From a methodolog-
ical point of view, this is interesting for two reasons: 1) it shows
thatusefulcosmologicalconstrainscanbeobtainedalreadyfrom
relatively small samples of clusters of galaxies, demonstrating
the exceptional potential of this type of cosmological tests; and
2) already this modest sample is practically systematics-limited,
especially due to uncertainties in the mass estimation.
There are many factors that affect the scaling relations
and the intrinsic scatter of the cluster populations around
these relations: the presence of cool cores (Markevitch 1998;
O’Hara et al. 2006; Motl et al. 2005; Pratt et al. 2009), substruc-
tures and the cluster’s dynamical state (B¨ ohringer et al. 2010;
Jeltema et al. 2008) and additional non-gravitational physics
(Nagai 2006), etc. In addition, one has to account for the
Malmquist and Eddington bias when determining the scaling
relations from an X-ray selected sample of clusters by proper
treatment of the selection and mass functions (especially for
LX, Pacaud et al. 2007; Vikhlinin et al. 2009; Pratt et al. 2009;
Mantz et al. 2010a,b). As our cluster samples cover broader red-
shift ranges potential deviations from the self-similar evolution
of the scaling relations also become an important question.
In summary, in order to be able to well constrain cosmolog-
ical models with cluster samples we need: 1) large cluster sam-
ples covering redshifts beyond unity; 2) good knowledge of the
cluster selection function’s dependence on relevant observables
and the distributions of these observables in the cluster popula-
tion; 3) a reliable, low scatter mass-proxy with a known evolu-
tion in the redshift range of interest.
Surveying for clusters in SZE has a large potential with re-
gards to all three requirements, having an almost redshift in-
dependent selection very close to a selection function with a
fixed mass limit at all redshifts and a robust mass-proxy in the
YSZparameter. Two ground-based large-area cluster surveys are
currently underway: one by the South Pole Telescope (SPT)
and one by the Atacama Cosmology Telescope (ACT). Both
have already provided their first SZE-selected cluster samples
(Williamson et al. 2011; Vanderlinde et al. 2010; Marriage et al.
2010; Staniszewski et al. 2009) as well as observations of al-
ready known clusters (Plagge et al. 2010; Hincks et al. 2010).
Also the Planck space mission has delivered its first cluster cat-
alog (Planck Collaboration 2011a).
While the SZE surveying approach is a very interesting new
channel to perform cluster cosmology, there is still much work
to be done at these early stages to understand the systematics
like the influence of radio/sub-mm sources and primary CMB
fluctuations on the selection, the mass calibration and sensitivity
to cluster outskirts.
A multi-wavelength follow-up program of SZE selected
clusters is essential, but selection function studies require also
comparison of blind surveys. To this end we are conducting the
XMM-BCS cluster survey. The survey field covers a 14 deg2
area in the overlapregionof the SPT and ACT surveys.The field
has full coverage with the 4m CTIO telescope at Cerro Tololo,
Chile, in the frameworkof the Blanco CosmologySurvey(BCS)
in griz bands and Spitzer observations in the mid-infrared (mid-
IR). With this optical to mid-IR coverage we are able to provide
robust photometric redshift estimates out to redshifts ≈ 0.8 (≈ 1
once also the Spitzer data is included). The X-ray coverage con-
sists of XMM-Newton observations split into two distinct parts.
The 6 deg2core of the X-ray survey field was observed with 42
individual, standard pointings (with ∼ 10 ks effective exposure
time). In this work, we present an initial cluster catalog based on
these observations.
After SPT commenced its operations, it was soon found that
the mass threshold of contemporary SZE surveys is higher than
expected. In order to offer a larger overlap between the SZE and
X-ray selected cluster samples, we carried out an extension of
the X-ray survey by coveringan additional 8 deg2in three large-
area fields utilizing the new mosaic mode type of observations.
Theseobservationsallowedus tocoverasignificantlylargerarea
in a very time-efficient way. First results as well as details on the
analysisofthistypeofXMM-Newtonobservationsaredescribed
inˇSuhada et al. (2010). We demonstrate there the feasibility of
blindly detecting clusters foundwith current generationSZE ex-
periments in only ∼ 3 ks long XMM-Newton observations (in-
cluding tentative spectroscopic temperature measurements) in
the case of two SPT detected clusters. The final 14 deg2X-ray
cluster catalog is expectedto roughlydoublethe numberof clus-
ters inthe presentsample andthis sample will thenbeinteresting
also for its cosmology-constrainingpower.
The paper is organized as follows: in Sect. 2 and 3 we de-
scribe the analysis of the X-ray observations and cluster detec-
tion pipeline. The optical data, photometric redshift estimation
and spectroscopic campaign are detailed in Sect. 4. In Sect. 5
we provide our cluster sample, the physical parameters of the
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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
detected clusters and determine the survey’s preliminary sta-
tistical properties. We also cross-correlate our cluster catalog
with known sources and carry out a detailed comparison with
the optically selected sample of Menanteau et al. (2009) and
Menanteau et al. (2010) (M09 and M10 hereafter). Sect. 6 dis-
cusses the X-ray error budget and gives an outlook on the up-
coming work in the context of the XMM-BCS survey. We give
our conclusions in Sect. 5. In the appendices we provide ancil-
lary information for the individual clusters, a preliminary com-
parison of our simplified sensitivity function calculations with
realistic simulations and a cross-comparison with the XMM-
LSS cluster survey.
We adopt a ΛCDM cosmology with (ΩΛ,ΩM,w,H0) =
(0.7,0.3,−1,70 km s−1Mpc−1). Estimated physical parameters
are given in apertures corresponding to overdensities by factors
200 and 500 with respect to the critical density of the Universe
at the redshift of a given cluster. Throughoutthe article we refer
to objects in our sample as ”clusters” regardless of their mass.
The term ”group” will be used to refer to systems with masses
M200 ? 1014M⊙. We will refer to individual objects by their
identification number (ID). Proper object names are listed in
Table A.1.
2. XMM-Newton data reduction
TheXMM-Newtoncoverageof theXMM-BCS surveycorecon-
sists of 42 partially overlapping pointings with offsets of 22.8
arcmin covering a total area of about 6 deg2(see Fig. 1). The
observing time was allocated in the frame of an XMM-Newton
Large Program during AO6. Four additional observations were
carried out in AO7 to replace fields with large losses due to soft-
proton flaring. The observation of field F09 (Table 1) was car-
ried out in two parts. The total observing time amounts to ∼ 580
ks, with an average total nominal time per pointing of ∼ 15 ks
(including instrument setup time and high background periods).
Table1displaysthebasicinformationabouttheindividualpoint-
ings. The THIN filter was used in all observations.The EPIC PN
camera was operated in full frame mode.
The full XMM-BCS X-ray field is displayed in Fig. 1. The
core region presented in this work is inside the white boundaries
(regionF). RegionsA,B andC markthethreemosaicextensions
of the survey. The five missing fields in region F have been com-
pletelylostduetoflaring(F03,F05,F42)orhadlargetimelosses
due to flaring and have a very high residual quiescent soft pro-
ton contamination (F07 and F13). The point source subtracted
signal-to-noise map of the core region is displayed in Fig. 2 (see
e.g. Finoguenov et al. 2009; Bielby et al. 2010).
The EPIC data was processed with the XMM-Newton
StandardAnalysisSystem(SAS)version7.1.01.We reducedand
calibrated the raw observational data files with the SAS tasks
epchain for the EPIC PN detector and emchain for both MOS
detectors. Events in bad pixels, bad columns and close to the
chip gaps are excluded from further analysis.
The event lists were screened for high background periods
caused by soft protonflares with a two-step sigma clipping algo-
rithm(Pratt & Arnaud2003).We rejecttimeintervalswithback-
groundcount rates above the 3σ limit from the mean level in the
12 − 14 keV band for PN and 10 − 12 keV band for MOS1 and
MOS2. The mean backgroundcountrate is determinedby fitting
a Gaussian model to the distribution of counts in the light curve
binned in 100 second intervals. After this first cleaning step, we
apply the same 3σ clipping procedure in the 0.3 − 10 keV band
1We provide a test using the current SAS 11.0.0 in Sect. 6.1.
on 10 second binned light curves to conservatively remove time
intervalsaffected bylow energyflares. An exampleof a two-step
cleaned light curve is displayed in Fig. 3.
Time lost due to flaring in our observations amounts typi-
cally to ∼ 20% of the full effective observing time. Six observa-
tions of the initial fields from AO6 were too heavily affected by
the flaring even after the two step cleaning. Three of these fields
have been replaced by observations in AO7 (F01b, F02b, F35b);
the partially lost field F04 was reobserved as well.
Detection and analysis of faint diffuse sources like clusters
of galaxies in shallow surveys can be additionally affected by
low energy soft protons with a roughly constant flux. This so-
called quiescent soft proton background can not be detected
based on light curve screening due to its small temporal vari-
ations, especially not in observations with a short duration. In
orderto characterizepossiblecontaminationfromthis part ofthe
non-X-raybackground,we applied the diagnostics developedby
De Luca & Molendi (2004), based on flux ratios inside and out-
side the field of view of each detector.The vast majorityof fields
is not contaminated by the quiescent soft proton background at
allinanyofthedetectors.Fourfields(F04,F06,F16,F25)havea
slight2contamination with negligible effect on data analysis and
derived results. Fields F07, F13 have significant time losses due
to flaring periods (particularly in PN) and in addition are now
found to have strong residual quiescent contamination (> 30%).
There is no cluster in the present sample found in these fields.
The PN camera in field F32 is also significantly affected (∼ 39%
background enhancement). We identified two clusters (ID 476
and 139) in this observation. Since the results from the PN and
MOS cameras for these sources are in agreement within the er-
ror bars we conclude that our background model was able to
account for the enhancement. For more details on these sources
see Sect. A.2.
The double component background model (see Sect. 3.1)
used for the source detection and characterization can in prin-
ciple account to first order for such an enhanced background
by increasing the normalization of the background model. The
vignetting function of such particle background has a different
shape than the vignetting of the X-ray photons, but it is known
only tentatively. We expect the errors from such first order ap-
proximation to be small compared to other sources of uncer-
tainty (including the shot noise). We thus decide to include into
our analysis also fields with a strong residual quiescent contam-
ination, but parameters derived for sources in these fields should
be handled with caution.
We treat out-of-time-events (OOTE) for the PN detector in
a standard way. For each observation, we generate an OOTE
eventlist with the epchain and remove time periods identi-
fied in the two step cleaning process of the main PN eventlist.
Whenever an image is extracted from the PN eventlist, we ex-
tract also an image with the same selection criteria from the
OOTE eventlist, scale this image with a factor of 0.063 (full
frame readout mode) and subtract it from the PN image.
3. Source detection
As the main source detection algorithm we utilize the sliding
box technique and a maximum likelihood source fitting in their
improved implementation in the SAS tasks eboxdetect and
2Slightcontamination means< 15%increase inthebackground with
respect tonormal levels(for detailssee De Luca & Molendi 2004). This
enhancement is modeled in first approximation by our composite back-
ground model (see text further and Sect. 3.1).
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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Fig.1. Mosaic X-ray image of the 14 deg2XMM-Newton sky survey. The false color image was constructed from the surface
brightness in the 0.3 − 0.5, 0.5 − 2.0 and 2.0 − 4.5 keV bands. The white region (F) marks the 6 deg2core of the survey presented
in this work. Regions A, B and C constitute the extension of the survey by mosaic mode observations. The missing fields have
significant losses due to soft proton flares. Bluer fields are affected by enhanced background. Green circles mark the positions of
the present cluster sample and have a radius equal to r500.
emldetect. A detailed description of the work flow and con-
figuration of our detection pipeline, originally developed for the
XMM-Newton Distant Cluster Project (XDCP), can be found
in Fassbender (2008) and Fassbender et al. (2011a, submitted);
here we only summarize the main steps.
Source detection is carried out in three different schemes:
(i) the standard three band scheme: provides continuous, non-
overlapping coverage in three energy bands: 0.3 − 0.5 keV,
0.5 − 2.0 keV and 2.0 − 4.5 keV.
(ii) the optimized single band scheme: covers the 0.35−2.4 keV
band and was chosen to maximize the signal-to-noise-ratio for
clusters of galaxies with a large range of redshifts and tempera-
tures (see also Scharf 2002). This bandpass is expected to max-
imize the signal-to-noise-ratio especially for high redshift sys-
tems (z ? 1)
(iii) the five band spectral matched scheme: uses five partially
overlappingbands (0.3−0.5, 0.35−2.4, 0.5−2.0, 2.0−4.5 and
0.5−7.5 keV). This scheme is equivalent to a single band detec-
tion in the full 0.3 − 7.5 keV range, where the energy intervals
in the overlaps have higher weighting. The shape of the weight-
ing function roughly mimics the expected continuum spectrum
shape of a hot cluster (Fassbender 2008). This setup was used
only to confirm detections from the first two schemes and we do
not use any results derived from it in the current work.
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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Fig.2. The 0.5−2 keV band signal-to-noise ratio map of the XMM-BCS core region (region F in Fig. 1) smoothed with a Gaussian
of 32′′width. Circles indicate the r500radii of the detected clusters and are labeled with the cluster ID number in the catalog. Point
sources have been subtracted using the method of Finoguenov et al. (2009).
3.1. Source list generation
Inordertoobtaintherawsourcelists, weextractimagesfromthe
cleaned eventlist for each detector and each band required in the
given detection scheme (e.g. in the three band scheme three im-
ages for each detector, in total nine images per field). We run the
sliding box detection algorithm (eboxdetect in the so-called
local mode) on these images. The background for each poten-
tial source is estimated only locally in a detection cell of 5 × 5
pixels in 4 successive runs with the number of pixels per cell
doubledin each iteration. Sources detected by this procedureare
then excised, creating an image usable for proper background
estimation.
We modelthe backgroundof each detectorandbandindivid-
ually with a double component background model. This back-
ground model is a linear combination of two templates based on
vignetted and unvignetted exposure maps, taking into account
the sky X-ray background (vignetted component) and the par-
ticle and instrumental background (unvignetted in the first ap-
proximation).
The final sliding box detection is then run utilizing the fitted
background model instead of a locally estimated background.
Forall sourcesabovethe detectionthresholdwe carryouta max-
imum likelihood fitting (with the emldetect task). A beta pro-
file (Cavaliere & Fusco-Femiano 1976) with a fixed beta value
of β = 2/3 convolved with the two dimensional point-spread
function (PSF) is fitted to each source. The fit is carried out for
allthreedetectorsandallthebandsinthegivendetectionscheme
simultaneously. The free parameters of the fit are the source po-
sition, normalization of the model (for each detector and band)
and the core radius, θc, characterizing the source extent. If the
extent of the source is not statistically significant, the source is
refitted as a point source with extent fixed to zero.
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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Table 1. The individual XMM-Newton pointings. Quoted exposures are effective exposures with high background periods filtered
out.
Field ID RA (J2000)Dec (J2000) Exposure times (ks)
MOS1
observation lost due to flaring
7.8 10.4
observation lost due to flaring
10.2 13.2
observation lost due to flaring
5.26.9
3.1 12.7
observation lost due to flaring
5.6 10.6
4.49.6
9.311.0
2.3 6.0
7.39.8
9.7 12.6
7.2 9.7
10.8 13.5
2.310.6
10.8 13.9
7.4 9.9
3.2 11.7
7.3 10.0
11.3 15.2
7.48.9
10.5 13.9
4.8 8.2
11.814.3
7.3 10.0
7.5 10.0
15.220.6
9.412.1
6.111.9
7.19.8
7.39.9
12.716.4
7.4 10.0
11.6 13.6
13.015.9
9.111.9
observation lost due to flaring
7.711.4
8.611.8
7.59.9
8.711.5
5.56.8
9.412.2
9.912.5
observation lost due to flaring
OBSID
0505380101
0554561001
0505380201
0554560201
0505380301
0505380401
0554560901
0505380501
0505380601
0505380701
0505380801
0505380901
0505384801
0505381001
0505381101
0505381201
0505381301
0505381401
0505381501
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Internal
F01
F01b
F02
F02b
F03
F04
F04b
F05
F06
F07
F08
F09
F09b
F10
F11
F12
F13
F14
F15
F16
F17
F18
F19
F20
F21
F22
F23
F24
F25
F26
F27
F28
F29
F30
F31
F32
F33
F34
F35
F35b
F36
F37
F38
F39
F40
F41
F42
PN MOS2
23:21:38.4
23:22:00.1
23:24:23.5
23:24:43.8
23:27:07.0
23:29:50.6
23:30:11.7
23:32:34.6
23:35:39.3
23:20:49.3
23:23:31.4
23:26:12.7
23:26:11.6
23:28:55.3
23:31:37.8
23:34:19.5
23:37:01.4
23:19:29.9
23:22:09.7
23:24:50.3
23:27:29.7
23:30:10.5
23:32:51.0
23:35:31.3
23:38:12.0
23:18:20.7
23:20:58.9
23:23:37.8
23:26:16.6
23:28:55.2
23:31:34.3
23:34:12.9
23:36:51.9
23:19:41.6
23:22:18.6
23:24:56.1
23:27:32.7
23:30:10.6
23:32:29.0
23:32:47.7
23:35:25.6
23:21:08.8
23:23:44.6
23:25:58.1
23:28:56.6
23:31:32.4
23:33:49.9
-56:07:34.4
-56:09:03.3
-56:07:13.2
-56:09:03.0
-56:07:16.3
-56:07:16.0
-56:09:01.2
-56:07:12.8
-56:08:18.7
-55:45:35.1
-55:45:39.2
-55:46:10.2
-55:46:30.2
-55:45:39.2
-55:45:39.7
-55:45:42.6
-55:45:39.2
-55:23:01.1
-55:23:23.1
-55:23:26.3
-55:23:45.9
-55:23:41.1
-55:23:38.5
-55:23:44.6
-55:23:43.7
-55:00:13.1
-55:00:36.3
-55:00:35.5
-55:00:42.1
-55:00:49.1
-55:00:51.0
-55:00:55.7
-55:00:54.2
-54:37:27.7
-54:37:53.3
-54:37:52.3
-54:38:04.7
-54:38:00.9
-54:36:00.3
-54:38:05.8
-54:37:57.3
-54:15:02.4
-54:15:01.5
-54:14:20.2
-54:15:15.2
-54:15:13.7
-54:13:13.3
10.4
13.2
6.9
12.7
10.6
9.6
9.8
6.0
9.8
12.6
9.7
13.5
10.6
13.9
9.9
11.7
10.0
15.2
8.9
13.9
8.2
14.3
10.0
10.0
20.6
12.1
11.9
9.8
9.9
16.4
10.0
13.6
15.9
11.9
11.1
11.8
9.9
11.5
6.8
12.2
12.5
The detection likelihood of a source is given by the det ml
parameter in the eboxdetect and emldetect tasks, defined as
det ml = −lnPrand, where Prandis the probability of observed
countsarisingfrompurerandomPoissonianfluctuations.Ineach
step of the detection process, the minimum detection likelihood
is set to 6, roughly equivalent to a ? 3σ detection in terms of
signal-to-noise ratio.
The extent likelihood ext ml, defined analogously to char-
acterize the probability of the source being extended, is required
to be ≥ 3 in the three-band scheme and ≥ 5 in the single band
scheme (corresponding approximately to minimum extent sig-
nificances of ∼ 2σ and ∼ 3σ, respectively).
For a moredetaileddiscussionandjustification ofthe chosen
detectionschemes and thresholdswe referto Fassbender(2008),
who also demonstrates the performance of the described source
detection methods on over 450 archival XMM-Newton observa-
tions in the frameworkofthe XDCP project.A descriptionof the
usedSAStaskscanbefoundintheSAS 7.1.0referencemanual.3
Inthecurrentwork,we aimforthe best possiblesurveycom-
pleteness including the high redshift end of the cluster distri-
bution and reliable source classification especially close to the
detection thresholds. This is also helped by combining differ-
ent detection schemes and setting relatively low extent thresh-
olds. The increasing source contamination close to the detection
threshold is treated with careful screening using the optical data
3xmm.esac.esa.int/sas/7.1.0/
6

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Fig.3. Left: The black line shows the 10 second-binnedPN light curve in the 0.3−10 keV band for the field F04. The beginningof
the observationwas affected by flaring. The green curve shows the light curve after the two-step cleaning (see Sect. 2), which safely
removed all contaminated time intervals. Right: Examples of the detection pipeline products for field F04 in the 0.5 − 2 keV band
of the PN detector: a) counts image, b) double-componentbackgroundmodel, c) binary detection mask, d) reconstructionof all the
detected sources. The green circle (2 arcmin radius) marks the cluster ID 018.
and ancillary X-ray information (e.g. quality flags described in
Appendix A).
The detected sources create a raw master list of extended
source candidates. Each of these candidates is then screened vi-
sually with optical imaging data (4 band BCS imaging) and ac-
ceptedtothefinal clustercatalogonlyifa significantoverdensity
of galaxies in the photometric redshift space is found (Sect. 4).
The available Spitzer imaging for the whole field will be used in
the future to confirm z >1 systems, where the depth of the BCS
imaging is not sufficient anymore.
The purely X-ray based selection function will be devel-
oped in subsequentwork based on simulations, where complete-
ness and contamination of different detection schemes will be
studied. Guided by extensive simulations of X-ray observations
(M¨ uhlegger 2010), we will get a high precision description of
the survey selection function. A statistically well defined cluster
sample will be drawn from the current catalog (plus its 8 deg2
extension) and used to study the evolution of the cluster X-ray
luminosity function and perform cosmological tests.
3.1.1. Treatment of MOS CCDs in anomalous state
A special note is required concerning the anomalous states of
CCD#4 of the MOS1 and CCD#5 of the MOS2 detectors and
theireffect onextendedsourcedetections.Halfof ourfields have
the MOS2 CCD#5 in the anomalous state and ∼ 20% have an
anomalous MOS1 CCD#4 (some observations are affected by
both). These anomalous (”hot”) states are characterized by high
overallbackgroundcountrateswithatypicalhardnessratios.The
most affected are the soft bands (in our case 0.3 − 0.5 keV and
0.5 − 2 keV bands, see also Kuntz & Snowden 2008).
We check for the presence of a hot chip in an observa-
tion by comparing count rates extracted from the suspected
chip and the mean count rate of three other chips in symmet-
rical positions around the central chip (i.e. the mean count rate
of CCD#2, CCD#4, CCD#7 of MOS2 and CCD#3, CCD#5,
CCD#7 ofMOS1).Thesereferencechipswereselected, because
they best match the area, shape and position of the affected chips
(see middle panel of Fig. 4). The count rates calculated in the
0.3− 2.0 keV band from the three reference chips are then aver-
aged to reduce shot noise and a chip is flagged hot, if its count
rate is more than 10% higher than the mean count rate from the
reference chips. This threshold is chosen to be very conservative
and was found to perform excellently, since chips in anomalous
states have typically count rates 50 − 100% higher than the ref-
erence rate. The algorithm also automatically flags observations
where a bright source lies on a reference chip. In these cases we
manually excise the source and repeat the calculation to obtain
an unaffected count rate ratio.
The exceptionally high background of the hot chips leads to
many spurious extended source detections, when left untreated
(see Fig. 4). We flag sources as possibly spurious detections
caused by the presence of a hot chip if at the same time: 1) they
lie on a chip that was flagged hot, 2) are extended, 3) the de-
tection likelihood from the given hot MOS detector in the soft
bands (sum of the 0.3 − 0.5 and 0.5 − 2.0 keV bands) accounts
for more than 90% of the total detection likelihood and 4) the
sourcewouldbeunderourdetectionthresholdwithoutthe detec-
tion on the affected chip. We still visually checked every flagged
sourcealso inthe opticalimagesandconfirmedthe classification
of these sources as spurious.
An example of this procedure can be seen in Fig. 4. The ob-
servation of field F21 has a hot MOS2 CCD#5, clearly visible
as an enhanced background (in the raw image in the left panel
and in the model background in the middle). The 8 extended
sources detected on this chip were flagged as potentially spu-
7

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Fig.4.Left:Imageoffield F21takenbytheMOS2camerainthe0.5−2.0keVband.TheMOS2CCD#5is visiblyinananomalously
high (”hot”) state with an enhanced background.Sources detected in this field are marked by green circles. Sources with red circles
wereautomaticallyflaggedas possiblyspuriousdetectionscausedbythe presenceof thehotchip.Middle: A compositebackground
model for the same detector and band created by fitting the double component model independently to the CCD#5 and the rest of
the chips. The three blue-marked chips are the reference chips used to identify hot chips in the observations. Right: The ratio of
the total detection likelihood (log scale) from the MOS2 CCD#5 in the 0.3 − 0.5 and 0.5 − 2.0 keV bands to the total detection
likelihoods from all other detectors and bands (log scale). Blue bars show the confirmed clusters from our sample, the red bars
the 8 flagged sources from field F21 (from the left panel). The vertical line marks where the soft band MOS2 detection constitutes
90% of the total detection likelihoods in all detectors. The flagged sources were confirmed as spurious by the optical data. A single
confirmed cluster (ID 275) appears above the threshold, but is not flagged as spurious since it would have been above the detection
likelihood even without the MOS2 detection (i.e. not meeting all the required criteria described in Sect. 3.1.1).
rious based on the described criteria. The detection likelihood
ratio (the MOS 2 detection likelihood in the soft bands over the
total detectionlikelihood)ofthese 8 sourcesare displayedonthe
left panel of Fig. 4 (red) as comparedto the sample of confirmed
clusters in our sample (blue).
A similar criterion can be applied in principle also to spu-
rious point source detections. An additional improvement can
be achieved by weighting the input detection likelihoods by the
number of pixels in the detection aperture in order to avoid a
possible bias, if a source has a low detection likelihood in one of
the reference detectors only because it falls on a chip gap or is
(partially) out of the field-of-view.
We also make an attempt to model the high background of
thehot chipsbyfittingthe doublecomponentmodeltoa hotchip
(in first approximation)and another double componentmodel to
the remaining chips. The two parts of the backgroundmodel are
thencombinedtocreateacompositebackgroundmapforthefull
detector area (middle panel of Fig. 4). All the extended sources
on hot chips flagged as spurious with the described detection
likelihood test, are not detected when the composite background
maps are utilized, confirming the reliability of our classification.
The effect of using a composite background instead of a stan-
dardbackgroundon detectionscomingfrom the remaining,non-
anomalous chips is minor, since the two background models in
these areas differ typically by less than 5%, and only the soft-
est bands of each detection scheme are affected. For the source
characterizationin observations affected by hot chips we use ex-
clusively composite background maps.
3.2. Growth curve analysis
The X-ray count rate is the most direct cluster observable. With
an estimate of the energy conversion factor at hand (see also
Sect. 3.3) we can calculate from it the X-ray flux FX, which in
turn can be converted to X-ray luminosity LX. The luminosity is
0 50100 150200
r [arcsec]
0
2
4
6
fX(<r) [10−14 erg s−1cm−2]
PN
MOS1+MOS2
r500
rplat
Fplat
Fplat
PN
MOS1+MOS2
r500
rplat
Fig.5. Example of the growth curve analysis of source ID 018
(photo-z=0.39). The cluster’s redshift and luminosity are close
to the median values of the entire sample. The curves show the
encircledcumulativefluxas afunctionofradius(PN:bluecurve,
combined MOS: red, dot-dashed). The PN and MOS curves are
in good agreement. Dashed lines mark the flux measurement er-
ror bars which include the Poisson noise and an additional 5%
systematic error from the background estimation. The estimated
plateau flux is Fplat= 5.19×10−14erg s−1cm−2(horizontalline),
reached at rplat ∼ 90 arcsec. The vertical line signifies the esti-
mated r500radius of the source, r500= 0.6 Mpc (∼ 117 arcsec).
In this case, the plateau radius is slightly smaller than r500and
the flux and luminosity for r500had to be extrapolated from their
plateau values. The required extrapolation is only ∼ 2% in this
case. See Sect. 3.3 for details.
8

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
a key parameters since it allows us to calculate other important
physical parameters (particularly the mass of the system) from
scaling relations.
A typical cluster of galaxies in relatively shallow observa-
tions like ours appears as a faint diffuse source with typically
? 100 source photons registered (total from all three standard
bands and detectors). Thus in order to get a reliable measure-
ment of the flux and trace the emission of the cluster as far out
as possible, we have to employ a robust method. In this work we
utilize the growth curve method developed for the REFLEX and
NORAS cluster samples derived from the ROSAT all sky survey
by B¨ ohringer et al. (2000). Here we summarize the procedure.
For each source, we extract images, exposure maps and
background maps in the 0.5 − 2.0 keV band, excluding all point
sources detected by the pipeline. MOS1 and MOS2 products are
then directly co-added,since the differencein their responsema-
trices is small. We run the growth curve program on the PN and
co-added MOS images independently.
In this analysis we use the X-ray center coordinatesobtained
from the beta model fitting procedure in the source detection
step. We also explored the possibility of recentering by min-
imizing the dipole moment of the count distribution (see e.g.
B¨ ohringer et al. 2010). This procedure usually yielded centers
very close to the best-fit beta model coordinates, but for faint
sources oftencompletelydiverged.The best-fit coordinateswere
always found to be a good description of the detected X-ray
emission centroid.
Countsareextractedfromtheimageinconcentricringsstart-
ing from the center and scaled by the exposure time. In this
way we obtain the total (source + background) count rate pro-
file. The expected background count rate is estimated from the
model backgroundmap and subtracted for each ring fromthe to-
tal countrate,obtainingthe sourcecountrate profile.Thegrowth
curve is the cumulative backgroundsubtracted source count rate
profile. An example of a growth curve is displayed in Fig. 5
(shown here in flux units using the energy conversion factor cal-
culated as described in Sect. 3.3).
We term the radius of the full aperture inside which a stable
growth curve can be obtained, the extraction radius rext(typi-
cally 150 − 200 arcsec). It is adjusted for each source individu-
ally (increased for brightest, most extended sources or trimmed
for sources close to the edge of FOV or to a partially blended
systems) and includes the source itself as well as enough sky
region to check the reliability of the double component back-
ground subtraction.
If the background model describes the local background ac-
curately, the growth curve levels off to a flat plateau at the outer
edge of the source. To estimate the total detected cluster emis-
sion, we first calculate the significance radius rsig, defined as the
radius outside which the source signal increases less than the 1σ
uncertainty in the count rate. The significance radius thus gives
the outermost radius where the potential increase of the growth
curve becomes less than 1σ significant.
To alleviate the effect of shot noise, rsig is determined by
smoothing the growth curve in 20 and 48 arcsec windows (5 and
12pixelsrespectively).Formost clustersthetwo estimates arein
agreement. In the remaining cases, the local backgroundusually
exhibitsirregularfeatures not capturedby the doublecomponent
model and we select the more appropriate rsigand plateau after
visual inspection.
In addition, a single multiplicative correction factor to the
background model can be set, if the plateau exhibits a signif-
icant residual slope. This additional factor corrects the over-
all normalization of the double component model locally in-
side rext. The average background correction factors are −2%
(i.e. a 2% decrease compared to the default double component
background) for PN and 0% for MOS (with standard deviations
7% and 8%, respectively). More than 3/4 of the present sam-
ple have correction factors smaller than 10%. Reiprich (2001)
and Reiprich & B¨ ohringer (2002) used a similar correction pro-
cedure utilizing a second order polynomial to obtain stable
plateaus. In our case, a simple correction factor turned out to
be sufficient and not leading to backgroundover-fitting.
After setting the background correction, the total source
count rate is estimated as the count rate of the plateau. The flat
plateau of the growth curve outside rsigis then fitted with a line.
Ifthe slopeoftheline is less than0.8%perradialbin,the plateau
fit is accepted and the plateau count rate CTRplatis estimated as
the mean of the fitted line. If the slope is still not negligible,
an additional attempt is made to find a stable plateau by itera-
tively removing the outermost and innermost (still outside rsig)
bins. We note that in ∼ 80% of cases the first simple fit is fully
acceptable and no further iterations are necessary. For more de-
tailed description of the iterative process and quality flags of the
plateau fit see Sect. A in the appendix. The plateau radius rplat,
is defined simply as the radial distance where the growth curve
first reaches CTRplat.
We provide a performance test of our X-ray photome-
try method on the example of the XMM-LSS cluster catalog
(Pacaud et al. 2007) in Appendix C.2. The main advantages of
the growth curve method thus are: (i) Excellent sensitivity al-
lowing us to trace cluster emission to the outermost faint out-
skirts. (ii) It makes no assumptions about the source profile un-
like methodsbased on beta modelfitting, which is fully degener-
ate in the regimewith < 400−500counts andis knownnot to be
an appropriate description of cluster emission for irregular and
cool core clusters. (iii) The method allows to check and correct
the background modelling which is done for the whole field of
view, by adjusting several parameters to the conditions local to
each analyzed source. (iv) The PN and combined MOS growth
curves are treated completely independently. Their comparison
providesuswithanimportantconsistencycheckandallowsusto
treat instrument specific features in the backgroundseparately.
3.3. Physical parameter estimation
With a stable PN and MOS growth curve at hand we determine
all the relevant physical parameters of the clusters (see Table 2)
in an iterative way:
1) The physical parameters are set to their initial values
(r500,T500) = (0.5 Mpc,2.5 keV).
2) The physical aperture radius is converted to arcseconds
using the assumed cosmologicalparameters.The total countrate
inside this radius is estimated from the PN and MOS growth
curves in the 0.5 − 2 keV band.
3) The count rates are converted to flux with an energy
conversion factor (ECF) calculated assuming a MEKAL spec-
tral model (Mewe et al. 1985; Kaastra 1992; Liedahl et al. 1995)
with abundance set to 0.3 times solar abundance, temperature
equal to the trial T500value and redshift set to the photo-z value
(or spectroscopic redshift where available).
To account for the spatial variation of the spectral response
of the detectors we calculate a response matrix for each source
individually in a 150 arcsec aperture centered on the source for
the THIN filter. The MOS2 response matrix is used to calculate
the ECF of the co-added MOS count rates.
9

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
This procedure is used to estimate the 0.5− 2 keV and bolo-
metric fluxes for PN and MOS and the same model is also used
to convert the fluxes to luminosities.
4) In some cases the estimated value of r500in the given iter-
ation is larger than rplat(i.e. larger than the region with directly
measurable emission), and therefore an extrapolation factor has
to be applied to the flux and luminosityestimates. We correct for
the missing flux between the plateau radius and current iteration
estimate of r500by extrapolatingthe source emission assuming a
beta model. The β and rcoreparametersof the beta model are cal-
culated using the scaling relations of Reiprich (2001) (see also
Reiprich & B¨ ohringer 2002; Finoguenov et al. 2007):
rcore= 0.07 × r500
?
T
1keV
?0.63
and β = 0.4
?
T
1keV
?1/3
.
(1)
10

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Table 2. Physical parameters of the clusters sample. Fluxes and luminosities are given for the 0.5−2 keV band. See Appendix A.2 for notes on individual sources. Notes:†the cluster was detected
in observations strongly affected by flaring;⋄the cluster is heavily affected by blending with a nearby source. Notes for redshifts:aspectroscopic redshift;ba high redshift system for which a secure
photometric redshift estimate is not possible from the current photometric catalog (the listed parameters are tentative estimates).
IDR.A. (J2000)
(deg)
351.8070
352.4828
352.1778
352.0448
352.6538
353.4388
353.5130
349.8214
349.2212
350.9631
350.6286
351.8470
351.5779
352.1748
352.2366
353.0185
351.9058
352.5161
351.6393
351.8492
352.0084
350.5036
351.3953
352.5015
352.4168
353.8815
353.6032
353.5240
350.5425
351.0160
353.6991
354.2119
353.7523
353.6200
353.9763
351.3891
354.0839
352.1177
351.4166
349.9334
353.0628
350.5456
353.8357
353.5258
353.1806
351.0815
Dec (J2000)
(deg)
−56.0615
−56.1360
−55.5662
−55.8400
−55.7270
−55.6387
−55.8156
−55.3244
−54.9036
−54.8923
−54.2691
−55.2624
−55.3859
−55.2234
−55.4081
−55.2120
−54.2705
−54.2388
−55.0206
−55.0648
−54.9292
−54.7500
−54.7212
−54.6184
−54.7886
−54.5865
−54.4586
−55.7859
−55.4199
−55.0225
−55.2736
−55.2988
−54.9164
−54.6066
−56.0928
−55.7327
−55.5189
−54.2472
−54.7412
−54.6400
−54.7006
−56.3127
−55.5442
−54.7310
−54.8297
−55.4305
z
rplat
Fplat
r500
(kpc)
577 ± 54
633 ± 58
702 ± 64
593 ± 54
536 ± 50
560 ± 53
503 ± 47
586 ± 53
787 ± 72
560 ± 53
815 ± 74
497 ± 47
526 ± 50
679 ± 63
481 ± 47
583 ± 54
510 ± 48
605 ± 56
643 ± 59
499 ± 46
571 ± 53
673 ± 62
517 ± 48
654 ± 59
448 ± 42
614 ± 56
617 ± 58
451 ± 43
506 ± 47
500 ± 48
541 ± 51
501 ± 54
580 ± 56
596 ± 55
468 ± 47
455 ± 44
521 ± 50
387 ± 39
508 ± 47
540 ± 50
574 ± 54
568 ± 54
441 ± 46
469 ± 52
629 ± 61
443 ± 46
F500
L500
T500
(keV)
3.4 ± 1.0
2.3 ± 0.7
4.2 ± 1.2
3.0 ± 0.9
1.5 ± 0.4
2.4 ± 0.7
1.5 ± 0.4
1.6 ± 0.5
3.6 ± 1.0
2.6 ± 0.7
2.9 ± 0.8
2.3 ± 0.7
2.0 ± 0.6
2.7 ± 0.8
1.6 ± 0.5
1.7 ± 0.5
2.8 ± 0.8
2.3 ± 0.7
2.4 ± 0.7
1.2 ± 0.4
3.3 ± 0.9
2.5 ± 0.7
1.3 ± 0.4
2.0 ± 0.6
1.0 ± 0.3
2.8 ± 0.8
2.5 ± 0.7
1.9 ± 0.5
1.4 ± 0.4
1.8 ± 0.5
1.5 ± 0.4
1.7 ± 0.5
2.4 ± 0.7
2.2 ± 0.6
1.5 ± 0.4
1.0 ± 0.3
2.2 ± 0.6
0.7 ± 0.2
1.2 ± 0.3
2.0 ± 0.6
1.7 ± 0.5
2.7 ± 0.8
1.1 ± 0.3
1.1 ± 0.3
2.7 ± 0.8
1.0 ± 0.3
M500
Y500
M200
(photo.)
0.97 ± 0.10
0.39 ± 0.04
0.83 ± 0.07
0.79 ± 0.05
0.28 ± 0.02
0.67 ± 0.05
0.39 ± 0.05
0.18 ± 0.04
0.44 ± 0.02
0.75 ± 0.07
0.152a
0.85 ± 0.12
0.63 ± 0.05
0.43 ± 0.04
0.58 ± 0.02
0.269a
1.02b
0.47 ± 0.06
0.42 ± 0.02
0.207a
0.96 ± 0.17
0.36 ± 0.02
0.169a
0.176a
0.139a
0.67 ± 0.06
0.55 ± 0.03
0.83 ± 0.09
0.346a
0.62 ± 0.03
0.29 ± 0.03
0.57 ± 0.04
0.60 ± 0.04
0.48 ± 0.06
0.53 ± 0.05
0.206a
0.71 ± 0.05
0.1a
0.102a
0.55 ± 0.05
0.269a
0.79 ± 0.06
0.35 ± 0.02
0.20 ± 0.02
0.57 ± 0.03
0.241a
(arcmin/r−1
0.7/0.6
1.5/0.7
1.6/1.0
1.1/0.9
1.7/0.8
1.2/0.9
1.1/0.7
2.3/0.7
2.4/1.1
1.7/1.3
2.9/0.6
0.4/0.4
0.9/0.7
2.5/1.3
0.8/0.7
1.3/0.6
0.7/0.7
1.4/0.8
1.9/1.0
0.8/0.3
1.3/1.1
2.4/1.1
1.9/0.6
2.9/0.8
0.9/0.3
0.8/0.6
1.4/0.9
0.7/0.7
1.1/0.6
0.8/0.7
1.7/0.8
0.9/0.7
1.8/1.2
2.0/1.2
0.7/0.6
1.3/0.6
0.9/0.7
1.6/0.5
2.3/0.5
0.7/0.5
1.5/0.6
1.0/0.8
0.8/0.5
1.6/0.7
1.7/1.0
1.1/0.5
500) (10−14erg s−1cm−2)
2.80 ± 0.42
5.14 ± 0.50
8.06 ± 0.68
2.94 ± 0.25
2.84 ± 0.45
2.02 ± 0.32
1.30 ± 0.20
9.12 ± 0.56
17.14 ± 1.15
2.22 ± 0.43
80.78 ± 1.64
1.01 ± 0.17
1.32 ± 0.23
8.50 ± 1.27
0.76 ± 0.18
4.83 ± 0.53
1.45 ± 0.21
3.77 ± 0.49
5.48 ± 0.41
2.58 ± 0.29
2.91 ± 0.34
8.29 ± 0.76
4.83 ± 0.65
18.25 ± 0.89
2.50 ± 0.41
3.35 ± 0.24
3.57 ± 0.54
0.55 ± 0.11
1.51 ± 0.19
0.97 ± 0.18
2.93 ± 0.45
0.98 ± 0.35
2.75 ± 0.63
3.56 ± 0.47
0.66 ± 0.17
1.59 ± 0.33
1.27 ± 0.25
1.98 ± 0.52
10.27 ± 1.18
1.55 ± 0.14
4.41 ± 0.71
2.26 ± 0.37
0.63 ± 0.21
1.99 ± 0.81
4.15 ± 1.13
1.05 ± 0.33
(10−14erg s−1cm−2)
2.85 ± 0.43
5.21 ± 0.51
8.00 ± 0.66
2.94 ± 0.25
2.89 ± 0.46
1.95 ± 0.31
1.33 ± 0.20
9.33 ± 0.57
16.75 ± 1.10
2.03 ± 0.35
83.43 ± 1.70
1.05 ± 0.18
1.34 ± 0.23
7.43 ± 1.01
0.78 ± 0.18
5.02 ± 0.55
1.47 ± 0.21
3.47 ± 0.47
5.38 ± 0.40
2.91 ± 0.33
2.74 ± 0.31
8.15 ± 0.68
5.01 ± 0.67
18.48 ± 0.91
2.97 ± 0.49
3.43 ± 0.24
3.58 ± 0.54
0.56 ± 0.11
1.56 ± 0.20
0.99 ± 0.18
2.95 ± 0.45
1.00 ± 0.36
2.44 ± 0.48
3.16 ± 0.40
0.67 ± 0.18
1.69 ± 0.36
1.29 ± 0.26
2.29 ± 0.60
11.04 ± 1.27
1.61 ± 0.14
4.53 ± 0.73
2.27 ± 0.37
0.68 ± 0.22
2.07 ± 0.85
4.07 ± 0.90
1.12 ± 0.35
(1043erg s−1)
11.8 ± 1.8
2.6 ± 0.3
21.5 ± 1.8
7.6 ± 0.6
0.7 ± 0.1
3.6 ± 0.6
0.7 ± 0.1
0.8 ± 0.1
10.5 ± 0.7
4.8 ± 0.8
5.0 ± 0.1
3.5 ± 0.6
2.2 ± 0.4
4.6 ± 0.6
1.1 ± 0.2
1.1 ± 0.1
7.3 ± 1.1
2.7 ± 0.4
3.2 ± 0.2
0.4 ± 0.1
11.1 ± 1.2
3.4 ± 0.3
0.4 ± 0.1
1.6 ± 0.1
0.2 ± 0.1
6.0 ± 0.4
4.0 ± 0.6
1.9 ± 0.4
0.6 ± 0.1
1.6 ± 0.3
0.8 ± 0.1
1.3 ± 0.5
3.4 ± 0.7
2.6 ± 0.3
0.8 ± 0.2
0.2 ± 0.1
2.8 ± 0.6
0.1 ± 0.1
0.3 ± 0.1
1.9 ± 0.2
1.0 ± 0.2
6.0 ± 1.0
0.3 ± 0.1
0.2 ± 0.1
4.9 ± 1.1
0.2 ± 0.1
(1013M⊙)
16.4 ± 4.6
10.9 ± 3.0
25.0 ± 6.9
14.4 ± 4.0
5.8 ± 1.6
10.5 ± 3.0
5.4 ± 1.5
6.8 ± 1.9
22.2 ± 6.1
11.6 ± 3.3
17.9 ± 4.9
9.1 ± 2.6
8.3 ± 2.4
14.1 ± 3.9
6.0 ± 1.8
7.4 ± 2.1
12.0 ± 3.4
10.4 ± 2.9
11.8 ± 3.2
4.4 ± 1.2
15.7 ± 4.4
12.6 ± 3.5
4.6 ± 1.3
9.4 ± 2.6
2.9 ± 0.8
13.9 ± 3.8
12.1 ± 3.4
6.6 ± 1.9
5.3 ± 1.5
7.0 ± 2.0
6.0 ± 1.7
6.7 ± 2.1
10.7 ± 3.1
10.1 ± 2.8
5.2 ± 1.6
3.3 ± 1.0
8.8 ± 2.5
1.8 ± 0.5
4.1 ± 1.1
8.1 ± 2.2
7.1 ± 2.0
12.6 ± 3.6
3.5 ± 1.1
3.6 ± 1.2
13.2 ± 3.8
3.1 ± 1.0
(1013M⊙keV)
4.8 ± 2.9
1.8 ± 1.1
10.8 ± 6.4
3.5 ± 2.1
0.5 ± 0.3
1.8 ± 1.1
0.4 ± 0.3
0.7 ± 0.4
7.5 ± 4.4
2.2 ± 1.4
4.5 ± 2.6
1.4 ± 0.9
1.1 ± 0.7
3.0 ± 1.8
0.6 ± 0.4
0.8 ± 0.5
2.6 ± 1.6
1.7 ± 1.0
2.1 ± 1.3
0.3 ± 0.2
4.4 ± 2.7
2.4 ± 1.4
0.3 ± 0.2
1.3 ± 0.7
0.1 ± 0.1
3.2 ± 1.9
2.3 ± 1.4
0.8 ± 0.5
0.4 ± 0.3
0.8 ± 0.5
0.5 ± 0.3
0.7 ± 0.5
1.9 ± 1.2
1.6 ± 0.9
0.4 ± 0.3
0.2 ± 0.1
1.3 ± 0.8
< 0.1
0.2 ± 0.1
1.1 ± 0.6
0.7 ± 0.4
2.7 ± 1.7
0.2 ± 0.1
0.2 ± 0.1
2.8 ± 1.7
0.1 ± 0.1
(1013M⊙)
24.3 ± 6.8
15.2 ± 4.2
37.0 ± 10.2
21.0 ± 5.8
7.9 ± 2.2
15.0 ± 4.2
7.5 ± 2.1
9.2 ± 2.5
31.6 ± 8.6
16.7 ± 4.7
24.6 ± 6.7
13.2 ± 3.7
11.8 ± 3.3
19.8 ± 5.5
8.3 ± 2.4
10.1 ± 2.8
17.7 ± 5.0
14.6 ± 4.1
16.5 ± 4.5
5.9 ± 1.6
23.3 ± 6.5
17.6 ± 4.8
6.2 ± 1.7
12.9 ± 3.5
3.9 ± 1.1
19.9 ± 5.5
17.2 ± 4.8
9.5 ± 2.7
7.2 ± 2.0
9.9 ± 2.8
8.2 ± 2.3
9.3 ± 3.0
15.3 ± 4.4
14.2 ± 3.9
7.2 ± 2.2
4.4 ± 1.3
12.6 ± 3.6
2.4 ± 0.7
5.5 ± 1.5
11.4 ± 3.2
9.7 ± 2.7
18.3 ± 5.2
4.8 ± 1.5
4.8 ± 1.6
18.7 ± 5.5
4.2 ± 1.3
011†
018
032
033
034
035
038⋄
039
044
069
070
081
082
088
090
094
109
110
126
127
132
136
139†⋄
150
152
156
158
210
227
245
275
287
288
357
386
430
444
457
476†⋄
502
511
527
528
538
543
547
11

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
The good sensitivity of the growth curve method allows us
to trace cluster emission out to large radii, therefore the re-
quired extrapolation is typically minor. The mean correction is
∼ 5%/ ∼ 6% for PN/MOS (∼ 46% at maximum).
In cases when r500≤ rplatno extrapolation is needed and the
fluxandluminosityestimates areindependentofanyassumption
about the spatial distribution of the source emission.
5) The source flux and luminosity are then obtained by aver-
aging the PN and MOS fluxes (luminosities) weighted by their
inverse squared errors. Sources for which the PN and MOS esti-
mates do not agree or one of the estimates is missing (e.g. source
outside of the FOV of a given detector) are flagged (Table A.1
in the appendix). An X-ray photometric quality flag is also as-
signed to each source based on the quality of the plateau fit, por-
tion of pixels outside the detection mask, presence of anomalous
features in the X-ray background and visual screening.
6) We then use this total (i.e. camera averaged) bolometric
luminosity value to calculate the temperature and mass of the
cluster for the next iteration by utilizing the L − T and L − M
scaling relations of Pratt et al. (2009):
M = 2 × 1014M⊙
?
h(z)−7/3L
1.38 × 1044ergs−1
?1/2.08
(2)
T = 5keV
?
h(z)−1L
7.13 × 1044ergs−1
?1/3.35
(3)
These relations were obtained by the BCES orthogonal fits
(Akritas & Bershady 1996), which do not treat T500(or M500)
as the independent variable, since our independent variable is in
fact LX(< r500). At this stage it is impossible to safely detect
and removeemission from possible cooling cores because of the
limited resolution of XMM-Newton. Therefore we opt not to do
so and use the relations that include also the core regions.
The Y500parameter is calculated from the Pratt et al. (2009)
relation:
YX= 2 × 1014M⊙keV
?
h(z)−9/5L
5.35 × 1044ergs−1
?1/1.04
.
(4)
7) The M500estimate is then used to obtain a new r500radius
fromr500=
sity of the Universe at redshift z. This new r500aperture is used
along the updated T500value to recalculate the fluxes and lumi-
nosities by repeating steps 1 − 7. The process is repeated until
convergenceto a final solution.
The final parameters are listed in Table 2. The table also
includes mass estimates in the r200aperture. An NFW profile
(Navarro et al. 1997) was assumed in order to extrapolate the
mass from the r500to r200aperture. The extrapolation factor was
calculated using the approximation derived by Hu & Kravtsov
(2003), where the parameters of the NFW profile are iteratively
estimated from the final (i.e. converged) M500mass by utilizing
the Bullock et al. (2001) relationfortheconcentrationparameter
calculation.
We providea discussionon the scalingrelations utilizedhere
and the error budget of our iterative method in Sect.6.1.
3?3M500/4π500ρC(z),whereρC(z)is thecriticalden-
4. Photometric redshift estimation
In order to measure the photometric redshifts (photo-zs) of
the X-ray selected systems in our sample, we applied the red-
sequence redshift estimator to the Blanco Cosmology Survey
(BCS) imaging data which covers two 50 deg2patches of the
southern sky and includes the full area of the present XMM-
Newton survey. The BCS is a 60 nights NOAO survey program
carried out from 2005-2008on the Blanco 4m telescope at Cerro
Tololo InteramericanObservatory.This griz survey was tuned to
the required depths to follow the passively evolving cluster red
sequence population to L∗+ 1 at 5σ significance out to z = 1.
The data were acquired using two offset layers of imaging in g
and r band, and three offset layers of imaging in i and z band.
TheBCS datahavebeenprocessedandcalibratedusingade-
velopment version of the Dark Energy Survey data management
system (DESDM v4, Mohr et al. 2008). The core processing
includes flat fielding, bias subtraction, illumination and fringe
corrections. Astrometric refinement uses the USNO-B2 catalog
and the AstrOmatic tool SCAMP (Bertin 2006). Reduced single
epochimages are combinedinto deep,coaddedimages using the
AstrOmatictoolSWarptogetherwithDESDM codethathomog-
enized the PSF to the median seeing within each tile/band com-
bination. Model fitting photometry using bulge+disk decompo-
sition was carried out using the extended version of SExtractor
(Bertin & Arnouts 1996) developed within DESDM to enable
PSF corrected model fitting to all detected objects on an image.
Photometriccalibrationwas carriedoutusingPSF modelmagni-
tudes calibrated using the stellar locus in the color space defined
by grizH where the H band data from 2MASS (Skrutskie et al.
2000) provides the overall photometric scale (Armstrong et al.
2010). A similar approach has been used for the processing
and calibration of all SPT cluster followup and redshift esti-
mation using Blanco 4m data, and the results have enabled de-
tailed studies of cluster galaxy properties (Zenteno et al. 2011)
as well as precise photometric redshift estimation (High et al.
2010; Williamson et al. 2011).
The full description of our photometric redshift method is
provided in Song et al. (2011); here we give its brief summary.
The red-sequence redshift estimator utilizes all available filters,
(g-, r-, i-, and z) to search for redshift peaks in the density dis-
tribution of galaxies within a radius of 0.8 Mpc centered on
the X-ray detection. To define the red-sequence at each red-
shift slice, we assume a single stellar population (SSP) model
by Bruzual & Charlot (2003) with a single burst of star forma-
tion at zf = 3 and passive evolution of red galaxies thereafter.
The SSP models are run with six distinct metallicities in order
to be able to model a tilted red sequence. The models are cal-
ibrated to reproduce the tilt of the color-magnitude relation for
the Coma cluster (e.g. Bower et al. 1992).
A single stellar population (SSP) model assuming a single
burst of star formation at zf = 3 and passive evolution of red
galaxies thereafter, by Bruzual & Charlot (2003) is used to de-
fine the red-sequence at each redshift slice, which is calibrated
to Coma cluster.
The contribution of backgroundgalaxies is estimated from a
surrounding 36′× 36′sky patch and statistically subtracted. For
each X-ray cluster candidatethe whole redshift rangefrom z = 0
to z = 1.05 is scanned through using simultaneously two colors
that bracket the 4000 Å break for a given redshift. This sup-
presses false overdensity peaks at transitional redshifts where
the 4000 Å break moves between two adjacent bands (e.g. the
transition between the g and r band around z ≈ 0.35). Once a
peak in redshift space is identified, we refine the redshift esti-
mate by fitting a Gaussian function to the redshift density distri-
bution. We then select cluster members in a stripe (0.05 width in
color) around the estimated red-sequence. The final cluster red-
shift value is calculated as the inverse color error weighted mean
12

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Table 3. Spectroscopic redshift for 12 clusters in the redshift
range z = 0 − 0.4. The redshifts were obtained from long-slit
spectroscopic observations at the NTT telescope. The redshifts
of the BCG galaxies are in the zA
a redshift for one additional member galaxy (zB
redshifts zphotoare taken from Table 2. For five systems we also
provide the photometric redshifts from the SCS survey (M09,
M10).
speccolumn. Four clusters have
spec). Photometric
ID
70
94
127
139
150
152
227
430
457
476
511
547
zA
spec
0.152
0.269
0.207
0.169
0.176
0.139
0.346
0.206
0.1
0.102
0.269
0.241
zB
spec
0.152
zphoto
zSCS
photo
0.120.17 ± 0.03
0.29 ± 0.04
0.22 ± 0.02
0.18 ± 0.01
0.20 ± 0.02
0.17 ± 0.02
0.35 ± 0.04
0.18 ± 0.01
0.10 ± 0.01
0.10 ± 0.01
0.26 ± 0.02
0.22 ± 0.02
0.209
0.1730.14
0.205
0.1
0.2
0.18
redshift of the selected member galaxies. This assures that the
reliability of the photo-z values for the whole system is always
better than for any individual galaxy. In a few cases two or more
solutions were found by our algorithm. For these systems we
visually check the obtained redshift distributions and select the
more likely solution given the positions of galaxies with respect
to the X-rayemission.An exampleof a final color-magnitudedi-
agram is shown in Fig. 6 for cluster ID 018 (its redshift is close
to the median redshift of the cluster sample).
The described photo-z estimation method allows us to mea-
sure the cluster redshift with good precision up to z ≈ 0.8 even
for low richness systems. The overalluncertaintyof the photo-zs
is on the ∼ 10% level. While care was taken to obtain reliable
results also for z ? 0.8 systems (see Fig. 7 for two examples),
here the already obtained Spitzer mid-infrared observations will
provide an important improvement in subsequent work. The fi-
nal photometric redshifts are presented in Table 2. A more de-
tailed analysis of optical counterparts for our systems including
optical luminosity and richness estimates will be presented in a
companion paper (Song et al., in prep.).
4.1. Spectroscopic redshifts
Spectroscopic redshifts are required to identify the clusters as
compact objects, to derive precise physical parameters and later
for cosmological modeling. In order to make a first step towards
these goals we have carried out spectroscopic observations of a
subsample of our clusters in the redshift range z = 0 − 0.4.
The observations made use of the EFOSC2 instrument at the
3.6 m New Technology Telescope (NTT) in La Silla, Chile. The
observations were carried out in September 2010, with typical
exposure times of 840 seconds (two spectra per cluster, 420 sec-
onds each). Our long-slit observations have been obtained us-
ing Grism #4 (wavelength range 4085− 7520Å). The slits (1.5′′
width)wereplacedontheBCG andanadditionalsuitablecluster
membercandidatebyrotatingtheslit.TheBCG galaxiesinthese
systems could be easily identified as the brightest red-sequence
galaxies always coincident with the X-ray centroid, allowing us
to safely anchor the cluster redshifts.
A standard reduction process was applied to the data us-
ing IRAF tasks.4The observationswere bias subtracted, cleaned
from cosmic rays, and flat fielded. For each galaxy we have ob-
tained two spectra which were sky subtracted and combined to
increase the signal-to-noise ratio. The wavelength calibration
was carried out by comparison with exposures of He and Ar
lamps.
The final spectra were then correlated with a database of
galaxy templates. In almost all cases the H and K lines and the
4000Å break were visible and used for visual check of the tem-
plate correction results. Spectroscopic redshifts have been se-
cured for a total of 12 BCG galaxies. Due to relatively short ex-
posures used only four systems a second member galaxy in the
slit had good signal-to-noise ratio in order to safely measure its
redshift.Inall fourcases thegalaxieswerefoundto haveconcor-
dant redshifts with the BCG value. The spectroscopic redshifts
ofthe galaxiesaresummarizedin Table 3alongwith ourphoto-z
estimates. We compare the two redshift sets in Sect. 4.2.
4.2. Comparison of spectroscopic and photometric redshifts
For a subsample of 12 clusters (z < 0.4) we have obtained spec-
troscopic redshifts of their BCG galaxy and in four systems also
for one additional member galaxy (Sect. 4.1). We compare the
spectroscopic redshifts with our photo-z values in Fig. 8 (left).
Our photometric values (red points) agree well within the error
bars with the spectroscopic redshifts of the BCG (green points,
brighter green points mark the clusters with two concordantred-
shifts). Blue points mark the photo-zs for five of the systems
obtained by the SCS survey (M09, M10). These values exhibit
a systematic bias toward lower redshifts, with a mean relative
difference of 19%. A similar trend is also visible in Fig. 13 (top
left) where we compare our photo-z values with the SCS mea-
surements for all clusters common to both samples.
The right panel of Fig. 8, displays the comparison of the ab-
solute difference of our photometricand spectroscopicestimates
in units of photo-zerror, D = |zphoto−zspec|/σphoto. A comparison
withaGaussianexpectationshowsanagreementatthe96%con-
fidence level, confirming both the good precision of our photo-z
estimates and the realistic description of their errors.
Thepresentspectroscopicsamplecoversonlypartofthered-
shift range and does not allow us to check the photometric red-
shift calibrationat higherredshift. However,the good agreement
at low z supports the photo-z method used. We also note, that
same photo-z method has been applied to a large numberof SPT
selected clusters extending to beyond z = 1, and the agreement
betweenspectroscopicandphotometricredshiftshas beenexcel-
lent (High et al. 2010; Williamson et al. 2011).
5. Results
5.1. Galaxy cluster sample
Table 2 provides the physical properties determined for the 46
clusters in the present sample. The measured X-ray luminosity
of the systems (Sect. 3.3) and the photometricand spectroscopic
redshifts (Sect. 4 and 4.1) are used as inputs for the cluster scal-
ing relations to estimate further physical parameters. Ancillary
X-ray information on the individual clusters can be found in
Table A.1.
The redshift, temperature and mass distributions are shown
in Fig. 9. We display the X-ray luminosity of our systems as a
4iraf.noao.edu
13

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
Fig.6. Left: Pseudo-colorimage of source ID 018 from the Blanco Cosmology Surveyin the gri bands. X-ray contours are overlaid
in white. The green circle shows the estimated 0.5 × r500. Right: Color-magnitude diagram (r − z vs. z) for the cluster ID 018. The
red points show the member galaxies used for the photo-z estimate and the red dotted line indicates the single stellar population
model at cluster redshift z = 0.39 (see text for details).
Fig.7. Pseudo-color images in the gri bands of the two X-ray detected (white contours) systems with secure photo-z values above
z > 0.9. Green circles mark the 0.5 × r500radius. Both clusters have a BCG coincident with the center of the X-ray detection.
functionof redshift in Fig. 10. The median redshift of the cluster
sampleis z = 0.47.Sixofthesystems havephotometricredshifts
z > 0.8. Three of these have redshifts consistent with z = 1, al-
though the photo-z uncertainty in this regime is large. The me-
dian temperature of the clusters is ∼ 2 keV and the median M500
mass 9×1013M⊙(basedonluminosityscalingrelations).We are
thus able to probe the cluster/group transition regime practically
at all redshifts out to z ≈ 1.
5.2. Survey sky-coverage
The simplest statistical characteristics of a cluster survey are its
area coverage as a function of limiting flux (sky-coverage func-
tion) and the cumulative surface density of the detected objects
above the given flux limit as a function of flux - the so-called
logN − logS relation.5
5We use the standard notation of this relation, but keep writing fXas
the source flux rather than S.
14

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R.ˇSuhada et al.: The XMM-BCS galaxy cluster survey
0.0 0.51.0
D
1.5
0.0
0.2
0.4
0.6
0.8
1.0
Cumulative count
normal distr.
data
Fig.8. Consistency test of our photometric redshift estimates with spectroscopic measurements in the redshift range z = 0 − 0.4.
Left: Comparison of our photometric redshift estimates (red, 1σ error bars) with spectroscopic values (green). Green stars mark
clusters for which we have two concordant galaxy redshifts, while green circles indicate clusters for which only the BCG has a
spectroscopic redshift. The photometric redshifts obtained by the SCS survey (M09, M10) are shown in blue. The x-axis displays
the cluster ID number. The objects are sorted in increasing redshift order. The bottom panel shows the residuals of the photo-z
values with respect to the spectroscopic measurement. Right: Cumulative histogram of the difference between the photometric and
spectroscopic redshift normalized by the 1σ uncertainty of the photo-z values, i.e. D = |zphoto− zspec|/σphoto. The dashed line shows
the expectation for the Gaussian distribution. Both curves are in good agreement, with a Kolmogorov-Smirnovtest confirming that
the distribution of the D values is Gaussian at the 96% confidence level.
In order to properly determine the survey’s sky coverage,
good knowledge of the survey’s selection function is necessary.
Forthe simplecase whentheselection functionis thefunctionof
only flux, the sky coverage is then the selection function of the
surveyscaled byits geometricarea.Especiallyforthecase ofex-
tended sources the situation is more complex,since the selection
function depends also on other parameters (e.g the source ex-
tent and off-axis angle). These effects can only be accounted for
by Monte Carlo simulations. At this moment, without the sim-
ulations at hand, we can still provide a preliminary, empirically
calibrated sky coverage calculation and cluster logN − logS re-
lation. We will demonstrate that these simple approaches show
good agreement with the design aims for the survey depth and
previous measurements of the cluster logN − logS function.
While our source detection pipeline utilizes multiple detec-
tion bands and likelihood thresholds (Sect. 3) we will for sim-
plicity (and ability to compare our results with published work)
characterize detections made in the standard 0.5 − 2 keV band
with a 3σ detection threshold and a 5σ extent significance.
Inorderto obtainthesurveysensitivityfunctionforextended
sources, we first calculate the point source sensitivity for each
field. This is a simpler task since it does not require treatment
of the source extent. We calculate the point source sensitivity
function by analytically invertingthe detection likelihood calcu-
lation (described in Sect. 3.1) and obtaining the minimal count-
rate necessary for a point source to be detected at the required
detection threshold given the local background and exposure in
the detection cell.
The procedure is repeated for each survey field and the re-
sults are combined for the whole survey area. In the areas where
two or more fields overlap, we compare the sensitivity maps
pixel-by-pixel taking the highest reached sensitivity (i.e. low-
est local count-rate limit) at the given position. This procedure
is chosen because the present catalog was derived from the de-
tection pipeline that ran on each field individually. An alterna-
tive approachis to combinethe fields beforedetection - reaching
slightly deeper flux-limits in the overlapping areas.6This comes
at the cost of losing the information on the local PSF shape used
bythe maximum-likelihoodfitting algorithm,since the same sky
location in two different observations is imaged at different off-
axis and position angles and thus with different PSF. Both ap-
proachesgivecomparableresults andwe optheretocharacterize
the main scheme (i.e. detection on individual fields).
The median point source sensitivity calculated in this way
for the whole survey area is 3.7 × 10−15erg s−1cm−2for an
energy-conversionfactor7of 1.5 × 10−12erg s−1cm−2. The cor-
responding sky coverage as a function of flux is displayed in
Fig. 11.
In the next step, we attempt to obtain a first order approxi-
mation to the sky coverage function for the extended sources by
a simple scaling to the point source function.In Fig. 10 (left) we
show the dependenceof the detection likelihood (i.e. the det ml
parameter) on the total detected source counts for point sources
and the confirmed clusters from our sample (full circles).
Photons from extended sources are distributed over a larger
areaandthusrequiremorecountstoreacha givendetectionlike-
lihood compared to point sources. For those, a simple linear re-
lation in the log-log plane is a good description of the counts-
det ml relation (dashed red line in Fig. 10). Since the number
of our clusters is small a similar linear relation for them is only
very weakly constrained. We therefore fix the slope to the value
from the point source fit leaving only the intercept as a free pa-
rameter and weighting the points by their counts error (solid red
line). The offset of the extended-source best-fit line translates to
6This was done for the ancillary catalog using the wavelet detection
algorithm.
7Assuming a power law spectrum with Γ = 1.7 and nH= 1.25×1020
cm−2(median value of the galactic column density in the survey field)
and using an on-axis PN response file.
15