Mass Estimation of Galaxy Clusters with Deep Learning. I. Sunyaev-Zel'dovich Effect
N Gupta, CL Reichardt
Astrophysical Journal | American Astronomical Society | Published : 2020
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..View full abstract
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Awarded by Australian Research Council
We acknowledge support from the Australian Research Council's Discovery Projects scheme (DP150103208). We thank Raffaella Capasso, Sebastian Grandis, Brian Nord, JoAo Caldeira, Sanjay Patil, and Federico Bianchini for their helpful feedback.