ASTROPHYSICS, ASTRONOMY, COSMOLOGY
A Deep Learning View of the Sensus of Galaxy Clusters in IllustrisTNG [arXiv] [bibTex] [doi] [media]
The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non cool core (NCC) clusters of galaxies, that are defined by their central cooling times. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92%, 81%, and 83%, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average BAcc=81%, or surface brightness concentrations, giving BAcc=73%. We use Class Activation Mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centers out to r ≈ 300 kpc and r ≈ 500 kpc to identify CC and NCC clusters, respectively.