Mass Estimation of Galaxy Clusters with Deep Learning. I. Sunyaev-Zel'dovich Effect

Abstract

We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev–Zel'dovich (SZ) effect signal and mass. The deep-learning algorithm used is mResUNet, which is a modified feed-forward deep-learning algorithm that broadly combines residual learning, convolution layers with different dilation rates, image regression activation, and a U-Net framework. We train and test the deep-learning model using simulated images of the microwave sky that include signals from the cosmic microwave background, dusty and radio galaxies, and instrumen..