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An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations

  • Published:2019-01-07 Issue:2 Volume:484 Page:1526-1537
  • ISSN:0035-8711
  • Container-title:Monthly Notices of the Royal Astronomical Society
  • language:en
  • Short-container-title:

Author:

Armitage Thomas J1ORCID,Kay Scott T1,Barnes David J2ORCID

Affiliation:

1. Jodrell Bank Centre for Astrophysics, School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, UK

2. Department of Physics, Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Funder

Science and Technology Facilities Council

Durham University

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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2. Cosmology with galaxy cluster properties using machine learning;Astronomy & Astrophysics;2024-06-24

3. Identifying galaxy cluster mergers with deep neural networks using idealized Compton-y and X-ray maps;Monthly Notices of the Royal Astronomical Society;2024-02-22

4. Deep learning-assisted Hubble parameter analysis;Modern Physics Letters A;2024-02-10

5. Galaxy stellar and total mass estimation using machine learning;Monthly Notices of the Royal Astronomical Society;2024-02-07
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