A deep learning view of the census of galaxy clusters in IllustrisTNG

TL;DR

This paper introduces a deep learning approach using ResNet-18 to classify galaxy clusters into cool core, weak cool core, and non cool core types from mock X-ray images, achieving high accuracy and revealing key discriminative regions.

Contribution

The study demonstrates that convolutional neural networks can effectively classify galaxy cluster types from X-ray images without spectral data, outperforming traditional methods.

Findings

Deep learning achieves over 90% accuracy in classifying cluster types.

CNN outperforms conventional classification methods based on gas density and surface brightness.

Network focuses on central regions, identifying features linked to AGN feedback and mergers.

Abstract

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. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. 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. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.