Abstract

I will present a machine-learning approach for estimating galaxy cluster masses from Chandra x-ray mock observations.  I will describe how a Convolutional Neural Network (CNN) -- a deep machine learning tool commonly used in image recognition tasks -- can be used to infer cluster masses from these images, reducing scatter in the mass estimates by up to 50%.  I will also show an interpretation tool, inspired by Google DeepDream, that can be used to gain some physical insight into what the CNN sees.