Analysing Thermal Spectra with Machine Learning

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Title:  A Novel Machine Approach to Disentangle Multi-Temperature Regions in Galaxy ClustersAuthors:  Carter L. Rhea, Julie Hlavacek-Larrondo, Laurence Perreault-Levasseur, Marie-Lou Gendron-Marsolais and Ralph KraftFirst Author’s Institution: Département de Physique, Université de Montréal, Québec, CanadaStatus: Accepted to AJ, open access on arXiv
Galaxy clusters are among the largest gravitationally bound structures in the Universe. One of their defining characteristics is that they tend to be embedded within a large reservoir of superheated gas, known as the intracluster medium (ICM). With temperatures up to 108 Kelvin, the ICM is a strong emitter of X-ray radiation. The resulting spectra is dominated by thermal bremßtrahlung radiation: radiation emitted when charged particles are decelerated. Characterising this thermal emission provides useful insights into the physical processes within the cluster, such as galaxy merging and AGN activity, as well as various physical parameters including temperature and metallicity. In order to obtain these parameters, one must first fit the observed spectra. However, the ICM is not necessarily uniform. Different regions are often characterised by multiple thermal components, hence requiring a mix of temperatures rather than a single temperature model to reproduce the observed spectra. The authors of today’s bite propose a new machine learning (ML) method …

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