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Title: Multiwavelength cluster mass estimates and machine learning

Abstract

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.

Authors:
ORCiD logo [1];  [2]
  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
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1703310
Resource Type:
Published Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 491 Journal Issue: 2; Journal ID: ISSN 0035-8711
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Cohn, J. D., and Battaglia, Nicholas. Multiwavelength cluster mass estimates and machine learning. United Kingdom: N. p., 2019. Web. https://doi.org/10.1093/mnras/stz3087.
Cohn, J. D., & Battaglia, Nicholas. Multiwavelength cluster mass estimates and machine learning. United Kingdom. https://doi.org/10.1093/mnras/stz3087
Cohn, J. D., and Battaglia, Nicholas. Tue . "Multiwavelength cluster mass estimates and machine learning". United Kingdom. https://doi.org/10.1093/mnras/stz3087.
@article{osti_1703310,
title = {Multiwavelength cluster mass estimates and machine learning},
author = {Cohn, J. D. and Battaglia, Nicholas},
abstractNote = {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.},
doi = {10.1093/mnras/stz3087},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 491,
place = {United Kingdom},
year = {2019},
month = {11}
}

Journal Article:
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https://doi.org/10.1093/mnras/stz3087

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