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CHEX-MATE: Turbulence in the intra-cluster medium from X-ray surface brightness fluctuations
Astronomy & Astrophysics ( IF 5.4 ) Pub Date : 2024-06-27 , DOI: 10.1051/0004-6361/202348701 S. Dupourqué , N. Clerc , E. Pointecouteau , D. Eckert , M. Gaspari , L. Lovisari , G. W. Pratt , E. Rasia , M. Rossetti , F. Vazza , M. Balboni , I. Bartalucci , H. Bourdin , F. De Luca , M. De Petris , S. Ettori , S. Ghizzardi , P. Mazzotta
Astronomy & Astrophysics ( IF 5.4 ) Pub Date : 2024-06-27 , DOI: 10.1051/0004-6361/202348701 S. Dupourqué , N. Clerc , E. Pointecouteau , D. Eckert , M. Gaspari , L. Lovisari , G. W. Pratt , E. Rasia , M. Rossetti , F. Vazza , M. Balboni , I. Bartalucci , H. Bourdin , F. De Luca , M. De Petris , S. Ettori , S. Ghizzardi , P. Mazzotta
The intra-cluster medium is prone to turbulent motion that will contribute to the non-thermal heating of the gas, complicating the use of galaxy clusters as cosmological probes. Indirect approaches can estimate the intensity and structure of turbulent motions by studying the associated fluctuations in gas density and X-ray surface brightness. In this work, we aim to constrain the gas density fluctuations occurring in the CHEX-MATE sample to obtain a detailed view of their properties in a large population of clusters. To do so, we use a simulation-based approach to constrain the parameters of the power spectrum of density fluctuations, assuming a Kolmogorov-like spectrum and including the stochastic nature of the fluctuation-related observables in the error budget. Using a machine-learning approach, we learn an approximate likelihood for each cluster. This method requires clusters not to be too disturbed, as fluctuations can originate from dynamic processes such as merging. Accordingly, we removed the less relaxed clusters (centroid shift w > 0.02) from our sample, resulting in a sample of 64 clusters. We defined different subsets of CHEX-MATE to determine properties of density fluctuations as a function of dynamical state, mass, and redshift, and we investigated the correlation with the presence or not of a radio halo. We found a positive correlation between the dynamical state and density fluctuation variance, a non-trivial behaviour with mass, and no specific trend with redshift or the presence of a radio halo. The injection scale is mostly constrained by the core region. The slope in the inertial range is consistent with the Kolmogorov theory. When interpreted as originating from turbulent motion, the density fluctuations in R500 yield an average Mach number of ℳ3D ≃ 0.4 ± 0.2, an associated non-thermal pressure support of Pturb/Ptot ≃ (9 ± 6)%, or a hydrostatic mass bias bturb ≃ 0.09 ± 0.06. These findings align with expectations from existing literature.
中文翻译：
CHEX-MATE：X 射线表面亮度波动导致星团内介质中的湍流
星系团内介质容易发生湍流运动，这将导致气体的非热加热，使星系团作为宇宙学探测器的使用变得复杂。间接方法可以通过研究气体密度和 X 射线表面亮度的相关波动来估计湍流运动的强度和结构。在这项工作中，我们的目标是限制 CHEX-MATE 样本中发生的气体密度波动，以获得大量簇中气体属性的详细视图。为此，我们使用基于模拟的方法来约束密度涨落功率谱的参数，假设类似柯尔莫哥洛夫的频谱，并将与涨落相关的可观测量的随机性质包括在误差预算中。使用机器学习方法，我们了解每个簇的近似可能性。该方法要求集群不要受到太大干扰，因为波动可能源于合并等动态过程。因此，我们从样本中删除了不太松弛的簇（质心位移 w > 0.02），得到了 64 个簇的样本。我们定义了 CHEX-MATE 的不同子集，以确定密度波动的特性作为动态状态、质量和红移的函数，并研究了与射电晕是否存在的相关性。我们发现动态状态和密度波动方差之间存在正相关性，与质量有关的非平凡行为，并且与红移或射电晕的存在没有特定趋势。注入规模主要受核心区限制。惯性范围内的斜率与柯尔莫哥洛夫理论一致。当解释为源于湍流运动时，R 500 yield an average Mach number of ℳ3D ≃ 0.4 ± 0.2, an associated non-thermal pressure support of Pturb/Ptot ≃ (9 ± 6)%, or a hydrostatic mass bias bturb ≃ 0.09 ± 0.06. These findings align with expectations from existing literature. 中的密度波动
更新日期：2024-06-27
中文翻译：
CHEX-MATE：X 射线表面亮度波动导致星团内介质中的湍流
星系团内介质容易发生湍流运动，这将导致气体的非热加热，使星系团作为宇宙学探测器的使用变得复杂。间接方法可以通过研究气体密度和 X 射线表面亮度的相关波动来估计湍流运动的强度和结构。在这项工作中，我们的目标是限制 CHEX-MATE 样本中发生的气体密度波动，以获得大量簇中气体属性的详细视图。为此，我们使用基于模拟的方法来约束密度涨落功率谱的参数，假设类似柯尔莫哥洛夫的频谱，并将与涨落相关的可观测量的随机性质包括在误差预算中。使用机器学习方法，我们了解每个簇的近似可能性。该方法要求集群不要受到太大干扰，因为波动可能源于合并等动态过程。因此，我们从样本中删除了不太松弛的簇（质心位移 w > 0.02），得到了 64 个簇的样本。我们定义了 CHEX-MATE 的不同子集，以确定密度波动的特性作为动态状态、质量和红移的函数，并研究了与射电晕是否存在的相关性。我们发现动态状态和密度波动方差之间存在正相关性，与质量有关的非平凡行为，并且与红移或射电晕的存在没有特定趋势。注入规模主要受核心区限制。惯性范围内的斜率与柯尔莫哥洛夫理论一致。当解释为源于湍流运动时，R 500 yield an average Mach number of ℳ3D ≃ 0.4 ± 0.2, an associated non-thermal pressure support of Pturb/Ptot ≃ (9 ± 6)%, or a hydrostatic mass bias bturb ≃ 0.09 ± 0.06. These findings align with expectations from existing literature. 中的密度波动
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