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The XXL Survey XXV. Cosmological analysis of the C1 cluster number counts

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arxiv 1810.01624 v1 pith:QPVNPQT6 submitted 2018-10-03 astro-ph.CO

classification astro-ph.CO
keywords clusterssampleanalysiscosmologicalplanckredshiftsurveycluster
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abstract

Context. We present an estimation of cosmological parameters with clusters of galaxies. Aims. We constrain the $\Omega_m$, $\sigma_8$, and $w$ parameters from a stand-alone sample of X-ray clusters detected in the 50 deg$^2$ XMM-XXL survey with a well-defined selection function. Methods. We analyse the redshift distribution of a sample comprising 178 high S/N clusters out to a redshift of unity. The cluster sample scaling relations are determined in a self-consistent manner. Results. In a lambda cold dark matter ($\Lambda$CDM) model, the cosmology favoured by the XXL clusters compares well with results derived from the Planck S-Z clusters for a totally different sample (mass/redshift range, selection biases, and scaling relations). However, with this preliminary sample and current mass calibration uncertainty, we find no inconsistency with the Planck CMB cosmology. If we relax the $w$ parameter, the Planck CMB uncertainties increase by a factor of $\sim$10 and become comparable with those from XXL clusters. Combining the two probes allows us to put constraints on $\Omega_m$=0.316$\pm$0.060, $\sigma_8$=0.814$\pm$0.054, and $w$=-1.02$\pm$0.20. Conclusions. This first self-consistent cosmological analysis of a sample of serendipitous XMM clusters already provides interesting insights into the constraining power of the XXL survey. Subsequent analysis will use a larger sample extending to lower confidence detections and include additional observable information, potentially improving posterior uncertainties by roughly a factor of 3.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 75 citations worldwide. Full citation record

  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...

  2. S-PLUS Clusters And Large-scale Environments (SCALE): II. PZWav versus redMaPPer identification of eRosita groups

    astro-ph.CO 2026-07 accept novelty 5.0 of 10

    A probabilistic Hausdorff-distance matching of PZWav/AME optical systems to eRASS1 X-ray contours yields multi-wavelength group/cluster catalogs with tunable purity and improved low-mass completeness versus redMaPPer.

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