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The cosmological analysis of X-ray cluster surveys: II- Application of the CR-HR method to the XMM archive

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arxiv 1109.4441 v2 pith:3IUOATV6 submitted 2011-09-20 astro-ph.CO

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keywords clusteranalysisarchivecosmologicalcr-hrevolutionscalingsurvey
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We have processed 2774 high-galactic observations from the XMM archive (as of May 2010) and extracted a serendipitous catalogue of some 850 clusters of galaxies based on purely X-ray criteria, following the methodology developed for the XMM-LSS survey. Restricting the sample to the highest signal-to-noise objects (347 clusters), we perform a cosmological analysis using the X-ray information only. The analysis consists in the modelling of the observed colour-magnitude (CR-HR) diagram constructed from cluster instrumental count-rates measured in the [0.5-2], [1-2] and [0.5-1] keV bands. A MCMC procedure simultaneously fits the cosmological parameters, the evolution of the cluster scaling laws and the selection effects. Our results are consistent with the sigma_8 and Omega_m values obtained by WMAP-5 and point toward a negative evolution of the cluster scaling relations with respect to the self-similar expectation. We are further able to constrain the cluster fractional radius xc0=r_c/r500c, to xc0=0.24 +/- 0.04. This study stresses again the critical role of selection effects in deriving cluster scaling relations, even in the local universe. Finally, we show that CR-HR method applied to the eRosita all-sky survey - provided that cluster photometric redshifts are available - will enable the determination of the equation of state of the dark energy at the level of the DETF stage IV predictions; simultaneously, the evolution of the cluster scaling-relations will be unambiguously determined. The XMM CLuster Archive Super Survey (XCLASS) serendipitous cluster catalogue is available online at: http://xmm-lss.in2p3.fr:8080/l4sdb/.

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  1. The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference

    astro-ph.CO 2025-07 conditional novelty 6.0 of 10

    A simulation-based forward model combining conditioned Lagrangian Deep Learning with simulation-based inference aims to bypass empirical scaling relations in X-ray cluster cosmology, demonstrated on CAMELS/IllustrisTN...

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