Clustering of CODEX clusters

Abstract

Context. The clustering of galaxy clusters links the spatial nonuniformity of dark matter halos to the growth of the primordial spectrum of perturbations. The amplitude of the clustering signal is widely used to estimate the halo mass of astrophysical objects. The advent of cluster mass calibrations enables using clustering in cosmological studies. Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the ROentgen SATellite (ROSAT) All-Sky Survey (RASS) and then identified as galaxy clusters using the code redMaPPer run on the photometry of the Sloan Digital Sky Survey (SDSS). We develop methods for precisely accounting for the sample selection effects on the clustering and demonstrate their robustness using numerical simulations. Methods. Using the clean CODEX sample, which was obtained by applying a redshift-dependent richness selection, we computed the two-point autocorrelation function of galaxy clusters in the 0.1 < z < 0.3 and 0.3 < z < 0.5 redshift bins. We compared the bias in the measured correlation function with values obtained in numerical simulations using a similar cluster mass range. Results. By fitting a power law, we measured a correlation length r 0 = 18.7 ± 1.1 and slope γ = 1.98 ± 0.14 for the correlation function in the full redshift range. By fixing the other cosmological parameters to their nine-year Wilkinson Microwave Anisotropy Probe (WMAP) values, we reproduced the observed shape of the correlation function under the following cosmological conditions: Ω m 0 = 0.22 +0.04 −0.03 and S 8 = σ 8 (Ω m 0 /0.3) 0.5 = 0.85 +0.10 −0.08 with estimated additional systematic errors of σ Ωm 0 = 0.02 and σ S 8 = 0.20. We illustrate the complementarity of clustering constraints by combining them with CODEX cosmological constraints based on the X-ray luminosity function, deriving Ω m 0 = 0.25 ± 0.01 and σ 8 = 0.81 +0.01 −0.02 with an estimated additional systematic error of σ Ωm 0 = 0.07 and σ σ 8 = 0.04. The mass calibration and statistical quality of the mass tracers are the dominant source of uncertainty.

AMICO galaxy clusters in KiDS-DR3: Constraints on cosmological parameters and on the normalisation of the mass-richness relation from clustering

“…The agreement within 1σ also holds for the cluster count analyses performed by Costanzi et al (2019), based on SDSS-DR8 data, and by Bocquet et al (2019), based on the 2500 deg 2 SPT-SZ survey data, as well as for the results derived from the cosmic shear analyses performed by Amon et al (2022) and Secco et al (2022) on DES-Y3 data, Hikage et al (2019) on HSC-Y1 data, and Asgari et al (2021) on KiDS-DR4 data. In addition, our result on S 8 agrees with the constraint by Lindholm et al (2021), namely S 8 = 0.85 +0.10 −0.08 , derived from the autocorrelation of Xray selected CODEX clusters. We also performed the analysis by assuming the halo bias model by Sheth et al (2001), obtaining S 8 = 0.79 +0.08 −0.08 .…”

Detecting clusters of galaxies and active galactic nuclei in an eROSITA all-sky survey digital twin

“…eROSITA is predicted to ultimately detect a total of about 10 5 clusters of galaxies after the final cumulative all-sky survey (eRASS:8), the largest sample of X-ray-selected galaxy clusters to date. This will allow a variety of studies involving the cluster X-ray luminosity function (Mullis et al 2004;Koens et al 2013;Finoguenov et al 2015;Adami et al 2018;Clerc et al 2020;Liu et al 2022), the clustering of galaxy clusters (Veropalumbo et al 2014;Marulli et al 2018Marulli et al , 2021Lindholm et al 2021), and provide powerful constraints on cosmological parameters such as the normalization of the power spectrum σ 8 and the matter content of the Universe Ω M (Borgani 2008;Vikhlinin et al 2009;Mantz et al 2015;Pierre et al 2016;Schellenberger & Reiprich 2017b;Pacaud et al 2018;Ider Chitham et al 2020;Garrel et al 2021). A prediction of the eROSITA cluster count cosmology capabilities is studied by Pillepich et al (2012Pillepich et al ( , 2018.…”

The SRG/eROSITA All-Sky Survey

“…A key ingredient of a clustering analysis is the prescription for the large-scale halo bias model. A common strategy consists of using a scaling relation approach to obtain halo masses from observables such as X-ray luminosity or richness (Lindholm et al 2021;Lesci et al 2022b). More recently, the development of emulators allows a fast prediction of clustering measurements that intrinsically include the bias (Nishimichi et al 2019;Sunayama et al 2023).…”

“…More recently, numerical simulations allowed the development of precise large-scale halo bias models (Tinker et al 2010;Bhattacharya et al 2011;Comparat et al 2017). As a result, clustering studies provide competitive cosmological constraints on their own (Borgani et al 1999;Moscardini et al 2000;Balaguera-Antolínez et al 2011;Hong et al 2016;Veropalumbo et al 2016;Marulli et al 2018Marulli et al , 2021Lindholm et al 2021;Ingoglia et al 2022;Lesci et al 2022bLesci et al , 2023Romanello et al 2024) and improve the constraining power from cluster counts alone (Mana et al 2013;Sartoris et al 2016;Pillepich et al 2018;Garrel et al 2022).…”