Context. Cosmological probes based on galaxy clusters rely on cluster number counts and large-scale structure information. X-ray cluster surveys are well suited for this purpose because they are far less affected by projection effects than optical surveys, and cluster properties can be predicted with good accuracy. Aims. The XMM Cluster Archive Super Survey, X-CLASS, is a serendipitous search of X-ray-detected galaxy clusters in 4176 XMM-Newton archival observations until August 2015. All observations are clipped to exposure times of 10 and 20 ks to obtain uniformity, and they span ∼269 deg 2 across the high-Galactic latitude sky (|b| > 20 o ). The main goal of the survey is the compilation of a wellselected cluster sample suitable for cosmological analyses. Methods. We describe the detection algorithm, the visual inspection, the verification process, and the redshift validation of the cluster sample, as well as the cluster selection function computed by simulations. We also present the various metadata that are released with the catalogue, along with two different count-rate measurements, an automatic one provided by the pipeline, and a more detailed and accurate interactive measurement. Furthermore, we provide the redshifts of 124 clusters obtained with a dedicated multi-object spectroscopic follow-up programme. Results. With this publication, we release the new X-CLASS catalogue of 1646 well-selected X-ray-detected clusters over a wide sky area, along with their selection function. The sample spans a wide redshift range, from the local Universe up to z ∼ 1.5, with 982 spectroscopically confirmed clusters, and over 70 clusters above z = 0.8. The redshift distribution peaks at z∼ 0.1, while if we remove the pointed observations it peaks at z ∼ 0.3. Because of its homogeneous selection and thorough verification, the cluster sample can be used for cosmological analyses, but also as a test-bed for the upcoming eROSITA observations and other current and future large-area cluster surveys. It is the first time that such a catalogue is made available to the community via an interactive database which gives access to a wealth of supplementary information, images, and data.

We present X-ray and optical properties of the optically confirmed galaxy cluster sample from the 3XMM/SDSS Stripe 82 cluster survey. The sample includes 54 galaxy clusters in the redshift range of 0.05-1.2, with a median redshift of 0.36. We first present the X-ray temperature and luminosity measurements that are used to investigate the X-ray luminosity-temperature relation. The slope and intercept of the relation are consistent with those published in the literature. Then, we investigate the optical properties of the cluster galaxies including their morphological analysis and the galaxy luminosity functions. The morphological content of cluster galaxies is investigated as a function of cluster mass and distance from the cluster center. No strong variation of the fraction of early and late type galaxies with cluster mass is observed. The fraction of early type galaxies as a function of cluster radius varies as expected. The individual galaxy luminosity functions (GLFs) of red sequence galaxies were studied in the five ugriz bands for 48 clusters. The GLFs were then stacked in three mass bins and two redshift bins. Twenty clusters of the present sample are studied for the first time in Xrays, and all are studied for the first time in the optical range. Altogether, our sample appears to have X-ray and optical properties typical of "average" cluster properties.

This article presents the results of a spectroscopic analysis of the X-CLASS-redMaPPer (XC1-RM) galaxy cluster sample. X-CLASS is a serendipitous search for clusters in X-ray wavebands based on the XMM–Newton archive, whereas redMaPPer is an optical cluster catalogue derived from the Sloan Digital Sky Survey (SDSS). The present sample comprises 92 X-ray extended sources identified in optical images within 1 arcmin separation. The area covered by the cluster sample is ∼ 27 deg2. The clusters span a wide redshift range (0.05 < z < 0.6) and 88 clusters benefit from spectrosopically confirmed redshifts using data from SDSS Data Release 14. We present an automated pipeline to derive the X-ray properties of the clusters in three distinct apertures: R500 (at fixed mass overdensity), Rfit (at fixed signal-to-noise ratio) and ${R}_{300\, {\rm kpc}}$ (fixed physical radius). The sample extends over wide temperature and luminosity ranges: from 1–10 keV and from 6 × 1042 to 11 × 1044 erg s−1, respectively. We investigate the luminosity–temperature (L–T) relation of the XC1-RM sample and find a slope equal to 3.03 ± 0.26. It is steeper than predicted by self-similar assumptions, in agreement with independent studies. A simplified approach is developed to estimate the amount and impact of selection biases that might be affecting our recoveredL–Tparameters. The result of this simulation process suggests that the measuredL–Trelation is biased to a steeper slope and higher normalization.

Galaxy clusters appear as extended sources in XMM–Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM–Newton X-ray observations with their optical counterparts from the all-sky Digitized Sky Survey. Our data set originates from the XMM CLuster Archive Super Survey (X-CLASS) survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterization. Our data set contains 1707 galaxy cluster candidates classified by experts. Additionally, we create an official Zooniverse citizen science project, The Hunt for Galaxy Clusters, to probe whether citizen volunteers could help in a challenging task of galaxy cluster visual confirmation. The project contained 1600 galaxy cluster candidates in total of which 404 overlap with the expert’s sample. The networks were trained on expert and Zooniverse data separately. The CNN test sample contains 85 spectroscopically confirmed clusters and 85 non-clusters that appear in both data sets. Our custom network achieved the best performance in the binary classification of clusters and non-clusters, acquiring accuracy of 90 per cent, averaged after 10 runs. The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging, and there is a lot of potential for future usage and improvements.