The 3XMM/SDSS Stripe 82 Galaxy Cluster Survey II. X-ray and optical properties of the cluster sample

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 X-rays, 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.Comment: accepted for publications in MNRA

X-ray properties of X-CLASS-redMaPPer galaxy cluster sample: The luminosity-temperature relation

International audienceThis paper presents results of a spectroscopic analysis of the X-CLASS-redMaPPer (XC1-RM) galaxy cluster sample. X-CLASS is a serendipitous search for clusters in the 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 $\sim$ 27 deg$^{2}$. 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: R\textsubscript{500} (at fixed mass overdensity), R\textsubscript{fit} (at fixed signal-to-noise ratio), R\textsubscript{300kpc} (fixed physical radius). The sample extends over wide temperature and luminosity ranges: from 1 to 10 keV and from 6$\times$10$^{42}$ to 11$\times$10$^{44}$ erg\,s$^{-1}$, respectively. We investigate the luminosity-temperature (L-T) relation of the XC1-RM sample and find a slope equals to 3.03 $\pm$ 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 which might be affecting our recovered L-T parameters. The result of this simulation process suggests that the measured L-T relation is biased to a steeper slope and higher normalization

Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

14 pages, 10 tables, 16 figures, accepted for publication in MNRASInternational audienceGalaxy 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 X-CLASS survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterisation. Our data set contains 1 707 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 1 600 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 %, 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