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 / Rossetti, M., Eckert, D., Gastaldello, F., Rasia, E., Pratt, G.W., Ettori, S., Molendi, S., Arnaud, M., Balboni, M., Bartalucci, I., Batalha, R.M., Borgani, S., Bourdin, H., De Grandi, S., De Luca, F., De Petris, M., Forman, W., Gaspari, M., Ghizzardi, S., Iqbal, A., et al.. - In: ASTRONOMY & ASTROPHYSICS. - ISSN 0004-6361. - 686:(2024), pp. 1-37. [10.1051/0004-6361/202348853]
CHEX-MATE: Robust reconstruction of temperature profiles in galaxy clusters with XMM-Newton
Gaspari M.;
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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Rossetti_2024_CHEXMATE_Tprofiles.pdf
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