The hot intra-cluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium comprised of various emitting components. Although previous studies of nearby galaxy clusters, such as the Perseus, the Coma, or the Virgo cluster, have demonstrated the need for multiple thermal components when spectroscopically fitting the ICM's X-ray emission, no systematic methodology for calculating the number of underlying components currently exists. In turn, underestimating or overestimating the number of components can cause systematic errors in the emission parameter estimations. In this paper, we present a novel approach to determining the number of components using an amalgam of machine learning techniques. Synthetic spectra containing a various number of underlying thermal components were created using well-established tools available from the \textit{Chandra} X-ray Observatory. The dimensions of the training set was initially reduced using the Principal Component Analysis and then categorized based on the number of underlying components using a Random Forest Classifier. Our trained and tested algorithm was subsequently applied to \textit{Chandra} X-ray observations of the Perseus cluster. Our results demonstrate that machine learning techniques can efficiently and reliably estimate the number of underlying thermal components in the spectra of galaxy clusters, regardless of the thermal model (MEKAL versus APEC). %and signal-to-noise ratio used. We also confirm that the core of the Perseus cluster contains a mix of differing underlying thermal components. We emphasize that although this methodology was trained and applied on \textit{Chandra} X-ray observations, it is readily portable to other current (e.g. XMM-Newton, eROSITA) and upcoming (e.g. Athena, Lynx, XRISM) X-ray telescopes. The code is publicly available at \url{this https URL}.

中文翻译：

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一种新的机器学习方法来解开星系团中的多温度区域

围绕星系团中心的热团内介质（ICM）是一种由各种发射成分组成的复杂介质。尽管之前对附近星系团（例如英仙座、彗发或处女座星系团）的研究已经证明在光谱拟合 ICM 的 X 射线发射时需要多个热成分，但目前没有系统的方法来计算潜在成分的数量存在。反过来，低估或高估组件的数量会导致排放参数估计中的系统误差。在本文中，我们提出了一种使用机器学习技术组合来确定组件数量的新方法。使用 \textit{Chandra} X 射线天文台提供的完善工具创建包含各种基础热成分的合成光谱。训练集的维度最初使用主成分分析减少，然后使用随机森林分类器根据基础成分的数量进行分类。我们经过训练和测试的算法随后应用于 Perseus 集群的 \textit{Chandra} X 射线观测。我们的结果表明，无论热模型如何（MEKAL 与 APEC），机器学习技术都可以有效且可靠地估计星系团光谱中潜在热成分的数量。% 和使用的信噪比。我们还确认 Perseus 星团的核心包含不同的底层热组件的混合。我们强调，尽管这种方法经过训练并应用于 \textit{Chandra} X 射线观测，但它很容易移植到其他当前（例如 XMM-Newton、eROSITA）和即将推出的（例如 Athena、Lynx、XRISM）X 射线望远镜. 该代码可在 \url{this https URL} 公开获得。