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CHEX-MATE: Robust reconstruction of temperature profiles in galaxy clusters with XMM-Newton

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arxiv 2402.18653 v1 pith:MZ6TWEM7 submitted 2024-02-28 astro-ph.CO astro-ph.HE

CHEX-MATE: Robust reconstruction of temperature profiles in galaxy clusters with XMM-Newton

classification astro-ph.CO astro-ph.HE
keywords measurementsdatatemperaturexmm-newtonbackgroundchex-mateclustersgalaxy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The "Cluster HEritage project with \xmm: Mass Assembly and Thermodynamics at the Endpoint of structure formation" (CHEX-MATE) is a multi-year Heritage program, to obtain homogeneous XMM-Newton observations of a representative sample of 118 galaxy clusters. The observations are tuned to reconstruct the distribution of the main thermodynamic quantities of the ICM up to $R_{500}$ and to obtain individual mass measurements, via the hydrostatic-equilibrium equation, with a precision of 15-20%. Temperature profiles are a necessary ingredient for the scientific goals of the project and it is thus crucial to derive the best possible temperature measurements from our data. This is why we have built a new pipeline for spectral extraction and analysis of XMM-Newton data, based on a new physically motivated background model and on a Bayesian approach with Markov Chain Monte Carlo (MCMC) methods, that we present in this paper for the first time. We applied this new method to a subset of 30 galaxy clusters representative of the CHEX-MATE sample and show that we can obtain reliable temperature measurements up to regions where the source intensity is as low as 20% of the background, keeping systematic errors below 10%. We compare the median profile of our sample and the best fit slope at large radii with literature results and we find a good agreement with other measurements based on XMM-Newton data. Conversely, when we exclude from our analysis the most contaminated regions, where the source intensity is below 20 of the background, we find significantly flatter profiles, in agreement with predictions from numerical simulations and independent measurements with a combination of Sunyaev-Zeldovich and X-ray imaging data.

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