A Deep Learning Approach to Galaxy Cluster X-ray Masses

TL;DR

This paper introduces a deep learning method using CNNs to estimate galaxy cluster masses from X-ray images, achieving lower scatter and bias than traditional methods, based on simulated data.

Contribution

The study demonstrates that CNNs can accurately estimate galaxy cluster masses from X-ray images without spectral data, outperforming traditional luminosity-based methods.

Findings

CNN achieves 8-12% scatter in mass estimation.

CNN exhibits minimal bias (-0.02 dex) compared to true masses.

CNN ignores cluster centers, focusing on outer regions.

Abstract

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7,896 Chandra X-ray mock observations, which are based on 329 massive clusters from the IllustrisTNG simulation. Our CNN learns from a low resolution spatial distribution of photon counts and does not use spectral information. Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (-0.02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all. In contrast, a more standard core-excised luminosity method achieves 15-18% scatter. We interpret the results with an approach inspired by Google DeepDream and find that the CNN ignores the central regions of clusters, which are known to have high scatter with mass.