Evolution of X-ray galaxy Cluster Properties in a Representative Sample (EXCPReS). Optimal binning for temperature profile extraction

TL;DR

This study introduces a new dynamic programming-based method for optimally binning X-ray temperature profiles of galaxy clusters, validated on a representative sample, and finds no evolution in temperature profile shape within the studied redshift range.

Contribution

The paper presents a novel dynamic programming algorithm for optimal binning of X-ray temperature profiles in galaxy clusters, improving analysis accuracy.

Findings

No evidence for evolution of temperature profile shape within redshift range

Derived a generic scaling relation between temperature error and surface brightness signal-to-noise ratio

Provided an average scaled 3D temperature profile for the sample

Abstract

We present XMM-Newton observations of a representative X-ray selected sample of 31 galaxy clusters at moderate redshift $(0.4<z<0.6)$, spanning the mass range $10^{14} < M_{\textrm 500} < 10^{15}$~M$_\odot$. This sample, EXCPRES (Evolution of X-ray galaxy Cluster Properties in a Representative Sample), is used to test and validate a new method to produce optimally-binned cluster X-ray temperature profiles. The method uses a dynamic programming algorithm, based on partitioning of the soft-band X-ray surface brightness profile, to obtain a binning scheme that optimally fulfils a given signal-to-noise threshold criterion out to large radius. From the resulting optimally-binned EXCPRES temperature profiles, and combining with those from the local REXCESS sample, we provide a generic scaling relation between the relative error on the temperature and the [0.3-2] keV surface brightness signal-to-noise ratio, and its dependence on temperature and redshift. We derive an average scaled 3D temperature profile for the sample. Comparing to the average scaled 3D temperature profiles from REXCESS, we find no evidence for evolution of the average profile shape within the redshift range that we probe.