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 \approx 300 kpc and r \approx 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 .