The X-CLASS survey: A catalogue of 1646 X-ray-selected galaxy clusters up to z$\sim$1.5

Observational cosmology has been coming into increasing focus over the last two decades, propelled by wide-area surveys, such as for example the Sloan Digital Sky Survey (SDSS; Blanton et al., 2017) and the Wide-field Infrared Survey Explorer (WISE; Wright et al., 2010), but also by modern observatories like eROSITA (Merloni et al., 2012; Predehl et al., 2021) and Euclid (Laureijs et al., 2011). All these surveys gain additional value from supplemental multi-wavelength observations, with a growing synergy between space- and ground-based observatories. Wide-area surveys constitute an essential asset for large-scale structure studies because they can provide a unique handle on the abundance of massive objects, a crucial element for cosmology. Nevertheless, the collection, analysis, and treatment of the data, in order to create valuable user-friendly catalogues and archives for the scientific community, is increasingly challenging and time-consuming, despite the availability of modern tools and computational capabilities. Therefore, such endeavours are rightfully dubbed ‘legacy surveys’.

In this framework, cosmologists seek large samples of galaxy clusters, spanning a wide range of masses and redshifts, in order to use them as cosmological probes. These probes mainly rely on cluster number counts and large-scale structure information to capture the evolution of the halo mass function and the halo spatial distribution across cosmic times. Galaxy clusters are the most massive gravitationally bound structures in the Universe. They are mainly dominated by dark matter ($\sim$ 85% of the total mass), while the hot X-ray-emitting intracluster medium (ICM) accounts for most of their baryonic mass (e.g. Plionis et al., 2008). Therefore, galaxy clusters can be identified in various wavelengths (e.g. Vikhlinin et al., 2009; Mantz et al., 2010; Rozo et al., 2010; Sehgal et al., 2011; Benson et al., 2013; Planck Collaboration et al., 2014), and X-ray surveys in particular have proven very effective in detecting large numbers of them (e.g. Fassbender et al., 2011; Willis et al., 2013; Pierre et al., 2004, 2016; Takey et al., 2016; Adami et al., 2018) including many at redshifts $z>1$, with the most distant clusters found up to a redshift of $z\sim 2$ (Santos et al., 2011; Mantz et al., 2018).

The X-ray selection, although possibly biased towards baryon-rich and relaxed clusters (e.g. Andreon et al., 2016; O’Sullivan et al., 2017), presents two main advantages: First, the cluster properties can be self-consistent and easily predicted by ab initio models, because the measurable X-ray parameters are closely related to the mass of the cluster (e.g. Frenk et al., 1990; van Haarlem et al., 1997). Second, cluster catalogues are hardly affected by projection effects (e.g. Ramos-Ceja et al., 2019), namely the inclusion of spurious sources resulting from the projection of unrelated systems along the line of sight, because the centrally concentrated X-ray emission clearly indicates the presence of gas trapped in the potential well of a cluster (e.g. Frenk et al., 1990; Reblinsky & Bartelmann, 1999).

Consequently, X-ray surveys have had a key role in the systematic search for galaxy clusters, initially with the historical HEAO-1 X-ray observatory (Piccinotti et al., 1982) and then with the Einstein observatory Medium Sensitivity Survey (Gioia et al., 1990; Henry et al., 1992). Many other important surveys have been conducted over the course of the last three decades, starting with the ROSAT observatory, which provided the instrumentation for REFLEX-I, II (Böhringer et al., 2001, 2014), NORAS (Böhringer et al., 2000), MACS (Ebeling et al., 2001), ROSAT-NEP (Henry et al., 2006) and CODEX (Finoguenov et al., 2020; Kirkpatrick et al., 2021), and then by XMM-Newton and Chandra observatories, which allowed COSMOS (Scoville et al., 2007; Finoguenov et al., 2007), XMM-LSS (Pierre et al., 2007; Clerc et al., 2014), XMM-BCS (Šuhada et al., 2012), XCS (Romer et al., 2001; Mehrtens et al., 2012), 2XMMi/SDSS (Takey et al., 2011, 2013, 2014), X-CLASS (Clerc et al., 2012, hereafter CS12), XDCP (Fassbender et al., 2011), and XMM-XXL (Pierre et al., 2016; Adami et al., 2018).

In this context, here we present the new X-CLASS catalogue of 1646 X-ray selected clusters followed by a public release. The catalogue is based on all 9333 XMM archival observations publicly available until August 2015. All observations were filtered following identical criteria to those in CS12: galactic latitudes above $20\deg$ and not located within the Magellanic Clouds and M31 areas, on sky exposure time larger than 5 ks in each of the three European Photon Imaging Camera (EPIC) detectors, and all three instruments in Imaging mode. This led to the selection of 4176 XMM pointings that went through an identical processing. In Sect. 2 we present the data processing, the resulting sample of galaxy cluster candidates, and their selection function. In Sect. 3 and 4 we describe the various steps of the screening of the candidates and further analysis of the sample. Finally, in Sect. 5 we describe the public database and in Sect. 6 we discuss the results and compare with other cluster catalogues. The full procedure is also illustrated in Fig. 1 in a flowchart. Throughout this paper, we use $H_{0}=70$ km s^-1 Mpc^-1, $\Omega_{m}=0.3$, and $\Omega_{\Lambda}=0.7$.

The data processing follows the lines of CS12 with improvements and modifications. We recall here the main steps involved and highlight the main changes. Basic parameters used during the various data processing steps can be found in Table 1.

The XMM observations were reduced with the latest calibration files available (August 2015) and light curves were created in the high-energy band to monitor the flaring time-intervals. An automated algorithm identified excess variance in the count-rate time histogram in order to determine acceptable count-rate levels which were later included in the GTIs (good time intervals). More specifically, event lists were filtered from proton and solar flares by creating the high-energy event light curves in the [12–14] keV band for MOS and the [10–12] keV band for PN, and flagging out periods of high event rates (rates greater than $3\sigma$ above the mean observation count rate). Event histograms and the corresponding light curves and cut limits are stored in the online database and are available for all pointings. While the above automated procedure is efficient in removing short periods of high flares, it may fail in observations with a high mean particle background. Therefore, although no systematic human verification is performed at this point, parameters and figures were stored in a database and the overall quality of each observation was subsequently inspected by eye during the screening procedure described in sect. 3.3.

The 4176 observations were homogenised by selecting intervals not contaminated by flares, such that their exposure time in each of the EPIC detectors amounts exactly to 10 ks or 20 ks. This ensures uniformity, enabling the calculation of selection functions. In what follows we refer to XMM observations associated to one of the above two versions as ‘pointings’. Therefore, each original XMM observation may deliver zero, one (10 ks), or two (10 ks and 20 ks) X-CLASS pointings. This resulted in a total of 2461 observations with a 10 ks exposure, of which 1309 also had a 20 ks version. The distribution of these pointings on the sky is shown in Fig. 2. A circular area of radius $13\arcmin$ around each pointing centre defines the geometric area of a pointing on the sky; sources are detected within this off-axis range only, thereby avoiding strongly vignetted areas on the detectors. The total survey geometrical area is computed by means of two-dimensional Monte-Carlo integration, accounting for the overlaps between pointings, and amounts to $269\leavevmode\nobreak\ \deg^{2}$. We note that pointed observations of clusters have been included in our observation list. Finally, they were processed twice with XAmin v3.5 pipeline (Faccioli et al., 2018) for both exposure times.

Source detection is a three-step process. During the first pass, images in the soft X-ray band, $[0.5-2]$ keV, are created and filtered with a wavelet algorithm, as described in Starck & Pierre (1998). According to that paper, this is the best filtering method for X-ray images that contain few photons and Poisson noise. Most importantly, it was demonstrated that this method is very effective in detecting low-flux extended X-ray sources, which is crucial for our short-exposure time survey. An example of such a filtered observation is presented in Fig. 3

During a second step, SExtractor (Bertin & Arnouts, 1996) was used for source detection. It provided a centroid estimate, the extent of the X-ray emission, and a rough measurement of brightness. The background level is iteratively estimated in image cells by 3$\sigma$ clipping and a full-resolution background map is constructed by bicubic-spline interpolation. Finally, these parameters served as input for a maximum likelihood fitting routine that applied several source models on the photon image in the soft X-ray band: in particular, a precise point-spread function (PSF) model and an extended $\beta$-model (Cavaliere & Fusco-Femiano, 1978) described by

where $EXT$ is the core radius in arc-seconds and $\beta=2/3$. For either a point-like or an extended source, the MEDIUM PSF model from the XMM calibration data is used. The SExtractor pixel segmentation mask was used to flag out pixels belonging to neighbouring sources included in the box. In contrast to previous XAmin versions, XAmin v3.5 fixed the position of the extended source fit at the value found by SExtractor. This selection yields 4812 extended X-ray sources.

Only sources classified as ”C1” in a pointing were used to build the catalogue. These are characterised by a value of their extended-detection likelihood greater than 32, an extent likelihood greater than 33, and an extent (best-fit core-radius) larger than $5^{\prime\prime}$ (for more details on these quantities see Faccioli et al., 2018). Simulations of XMM ‘empty’ cosmological fields demonstrated that such thresholds ensure pure samples of extended sources with a controllable selection function (Pacaud et al., 2006). However, XMM-Newton is generally not pointing towards empty fields. The diversity of sources and instrumental artefacts encountered in the archive makes the X-CLASS C1 sample more prone to contamination by non-cluster sources and this is the purpose of the visual inspection process to eliminate those spurious sources (see Sect. 3.3). At this stage, a given C1 detection may appear several times in the source list. This is either due to overlaps between XMM observations, because it appears in both exposure-time versions of the same pointing, or because it has been separated in multiple components, usually by sharp (small-scale) exposure variations due to one (or more) CCD gaps that caused the detection pipeline to segment the detection of extended sources into multiple areas. In the following sections, we describe the cluster selection function and the decontaminating procedure for the removal of duplicate and false detections. We note that clusters detected on observations with missing detectors were discarded from our list, but can be found in Table 4.

Owing to the uniform exposure time, simulations allow us to accurately determine the selection function of the X-CLASS cluster sample. However, a large number of extended X-ray detections can be due to various other sources, such as for example nearby galaxies, stars, active galactic nuclei (AGNs), double point-sources, and so on. Most of these sources are not modelled in the simulations and should be discarded before reaching a clean cluster sample. We describe this interactive procedure in the following section.

The X-CLASS database is built using the MySQL database management system and is accessible to a user through a Java Web Application. The database contains all catalogue meta-data, images and plots, which are available in PNG format generated from pipeline output. The cluster table is dynamically generated using a selection interface and is then displayed as an HTML web page. Several criteria (redshift, X-CLASS name, x-ray parameters, sky area, etc.) can be applied to refine (constrain) the cluster selection. For each cluster position, there are pre-configured requests to the external astronomical data servers (CDS, NED, NASA/IPAC IRSA) enabled to display optical counterparts and overlays within $3\leavevmode\nobreak\ arcmin$. A user manual along with relevant documentation is also available online, as well as the list of X-CLASS publications. The database is hosted in CC-IN2P3 (Centre de Calcul de l’IN2P3 at Lyon in France) and is publicly available online in https://xmm-xclass.in2p3.fr/.

On one hand, the homogeneous selection of the X-CLASS clusters on homogenised observations of 10 and 20 ks exposures simplifies the computation of their selection function and their use in cosmology studies. On the other hand, because of the above selection, the comparison with other similar datasets is difficult (see also relevant discussion in CS12, ). Nevertheless, within $1^{\prime}$ we cross-correlated the X-CLASS catalogue with other X-ray-selected cluster catalogues in order to examine the redshift agreement of the common sources. In Fig. 12 we present such a comparison with two catalogues of X-ray-detected clusters by XMM-Newton, XCS (Mehrtens et al., 2012), and 2XMMi/SDSS (Takey et al., 2014), and two by ROSAT, MCXC (Piffaretti et al., 2011) and CODEX (Finoguenov et al., 2020). There is good agreement between redshifts, especially with the MCXC catalogue. We note that in many cases redshifts were obtained from the same source. In cases of large discrepancies we examined our cluster identifications and redshift validation. In most of the discrepant cases, our more recent and updated data allow for a more accurate redshift estimation, while in a limited number of cases, the matched X-ray detections are correlated with different optical counterparts.

We also examined the X-ray luminosity distribution of the matched X-CLASS/XCS cluster sample in order to identify any systematic bias in our selection. In Fig. 13 we plot the distribution of $L_{500}[0.05-100]$ keV, as estimated in Mehrtens et al. (2012) within the $R_{500}$ radius, for both the matched and the full XCS sample. The two samples have very similar luminosity distributions, as also confirmed by a two-sample Kolmogorov-Smirnov test. Therefore, the matched sample is an unbiased subsample of the total XCS sample.

In addition we cross-correlated our catalogue with the 4XMM-DR10 catalogue (Webb et al., 2020). This catalogue includes a large number of extended X-ray sources and their fitting parameters, but does not include any redshift estimation or further examination of their nature. The large majority of the X-CLASS sources are correlated to an extended source in the 4XMM catalogue within $1^{\prime}$ radius. However, 191 sources are absent, most of which have a provisional status or no redshift information, and inspection of their optical counterpart places them at high redshift. In addition, they usually have a low value for the extent likelihood parameter, as computed by the XAmin pipeline. Nevertheless, a high fraction ($\sim$27%) of the uncorrelated clusters have a ‘confirmed’ or ‘photometric’ status, usually below $z=0.6$.

We present the detection pipeline, selection function, visual inspection, screening, and redshift confirmation of a large number of X-ray-detected galaxy clusters in 4176 archived XMM-Newton images. The total number reaches 1646 clusters over 269 deg² and the catalogue is publicly accessible via an interactive database constructed and maintained by the X-CLASS collaboration. The database not only allows the selection of a cluster subsample based on a large variety of criteria, but also gives access to a wealth of meta-data, images, plots, and supplementary information.

The homogeneous selection of the cluster samples over 10 and 20 ks exposures allows the use of the X-CLASS catalogue for cosmological analyses, provided the impact of pointed clusters is accounted for. In addition, the large sky area makes it suitable as a test bed for current and future large-area cluster surveys, such as the ones that will be carried out by the eROSITA (Merloni et al., 2012; Predehl et al., 2021), Athena (Nandra et al., 2013), and Euclid (Laureijs et al., 2011) missions, considering the large amount of human effort and interaction required for the compilation of the present catalogue, which could not be extended to the huge datasets of these missions.