The-Cluster HEritage project with XMM-Newton: Mass Assembly and Thermodynamics at the End point 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 intra-cluster medium up to R500 and to obtain individual mass measurements, via the hydrostatic-equilibrium equation, with a precision of 1520%. 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 methods, which 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 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.
CHEX-MATE: Robust reconstruction of temperature profiles in galaxy clusters with XMM-Newton / M. Rossetti, D. Eckert, F. Gastaldello, E. Rasia, G.W. Pratt, S. Ettori, S. Molendi, M. Arnaud, M. Balboni, I. Bartalucci, R.M. Batalha, S. Borgani, H. Bourdin, S. De Grandi, F. De Luca, M. De Petris, W. Forman, M. Gaspari, S. Ghizzardi, A. Iqbal, S. Kay, L. Lovisari, B.J. Maughan, P. Mazzotta, E. Pointecouteau, G. Riva, J. Sayers, M. Sereno. - In: ASTRONOMY & ASTROPHYSICS. - ISSN 0004-6361. - 686:(2024 Jun), pp. A68.1-A68.37. [10.1051/0004-6361/202348853]
CHEX-MATE: Robust reconstruction of temperature profiles in galaxy clusters with XMM-Newton
M. Rossetti
Primo
;G. Riva;
2024
Abstract
The-Cluster HEritage project with XMM-Newton: Mass Assembly and Thermodynamics at the End point 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 intra-cluster medium up to R500 and to obtain individual mass measurements, via the hydrostatic-equilibrium equation, with a precision of 1520%. 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 methods, which 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 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.File | Dimensione | Formato | |
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