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{https://github.com/XtraAstronomy/Pumpkin}.
