Multiwavelength cluster mass estimates and machine learning

Journal Article · 04 November 2019 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stz3087 · OSTI ID:1703310

Cohn, J. D.  ^[1]; Battaglia, Nicholas ^[2]

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1. Space Sciences Laboratory, University of California, Berkeley, CA 94720, USA, Theoretical Astrophysics Center, University of California, Berkeley, CA 94720, USA
2. Cornell University, Ithaca, NY 14853, USA

ABSTRACT One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is in contrast to finding mass estimates for each observable, normally by using a scaling relation, and then combining these scaling law based mass estimates using a likelihood. We also illustrate how the contributions of each observable to the accuracy of the resulting mass measurement can be compared via model-agnostic Importance Permutation values. Thirdly, as machine learning relies upon the accuracy of the training set in capturing observables, their correlations, and the observational selection function, and as the machine learning training set originates from simulations, two tests of whether a simulation’s correlations are consistent with observations are suggested and explored as well.

• Sponsoring Organization: USDOE
• OSTI ID: 1703310
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 491; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English