A Deep Learning Approach to Galaxy Cluster X-Ray Masses
Name
Ntampaka_2019_ApJ_876_82.pdf
Description
Published version
Size
13 MB
Format
Unknown
Checksum (MD5)
75217850774f187326b6c2bf46482795
Author(s)
Ntampaka, M.
ZuHone, J.
Eisenstein, D.
Nagai, D.
Vikhlinin, A.
Hernquist, L.
Marinacci, F.
Nelson, D.
Pakmor, R.
Pillepich, A.
Date Issued
May 2019
Journal
Astrophysical Journal
Publisher
American Astronomical Society
Version
Final published version
Abstract
© 2019. The American Astronomical Society. All rights reserved. We present a machine-learning (ML) approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep ML tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7896 Chandra X-ray mock observations, which are based on 329 massive clusters from the 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.
MIT Department
MIT Kavli Institute for Astrophysics and Space Research
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3847/1538-4357/ab14eb
