A Novel Machine Learning Approach to Disentangle Multi-Temperature Regions in Galaxy Clusters.

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}.