arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.18653astro-ph.COastro-ph.HE
收藏

CHEX-MATE:利用XMM-Newton对星系团温度剖面进行稳健重建

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. Bourdi… 展开作者

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

AI总结:

本文为CHEX-MATE项目构建了基于物理背景模型和MCMC贝叶斯方法的XMM-Newton数据分析新流水线,在30个星系团中实现了低至背景20%强度区域的可靠温度测量,并发现排除高污染区后温度剖面更平坦且与模拟预测一致。

摘要:

“Cluster HEritage project with XMM-Newton: Mass Assembly and Thermodynamics at the Endpoint of structure formation”(CHEX-MATE)是一项为期多年的Heritage计划,旨在获取118个代表性星系团样本的均匀XMM-Newton观测数据。这些观测经过调整,旨在重建ICM(星系团内介质)主要热力学量直至 $R_{500}$ 的分布,并通过流体静力学平衡方程获得精度为15-20%的单体质量测量。温度剖面是该项目科学目标的必要成分,因此从我们的数据中提取尽可能好的温度测量至关重要。为此,我们基于一个新的具有物理动机的背景模型以及采用马尔可夫链蒙特卡洛(MCMC)方法的贝叶斯方法,构建了一条用于XMM-Newton数据谱提取与分析的新流水线,并在本文中首次予以展示。我们将此新方法应用于代表CHEX-MATE样本的30个星系团子集,结果表明,在源强度低至背景20%的区域,我们依然能获得可靠的温度测量,并将系统误差保持在10%以下。我们将样本的中位数剖面与大半径处的最佳拟合斜率同文献结果进行了比较,发现其与其他基于XMM-Newton数据的测量结果高度一致。相反,当我们在分析中排除源强度低于背景20%的受污染最严重区域时,我们发现剖面明显更加平坦,这与数值模拟的预测以及结合Sunyaev-Zeldovich和X射线成像数据的独立测量结果相一致。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑