Cited by 3Galaxies: Formation, Evolution, PhenomenaStellar, planetary, and galactic studiesDark Matter and Cosmic Phenomena

Machine Learning the Dark Matter Halo Mass of Milky Way-Like Systems

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Hayati, Elaheh, Behroozi, Peter, Patel, Ekta·2024·The Open Journal of Astrophysics

Abstract

Despite the Milky Way’s proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being in dynamical equilibrium as well as nearby galaxies being true satellites of the Galaxy, and/or the need to find large samples of Milky Way analogs in simulations.Here, we propose a new technique based on neural networks that obtains high precision (<0.14 dex mass uncertainty with perfect measurements of 30 neighboring galaxies; <0.14 dex including fiducial observational errors) without assuming halo dynamical equilibrium or that neighboring galaxies are all satellites, and which can use information from a wide variety of simulated halos (even those dissimilar to the Milky Way) to improve its performance. This method uses only observable information including satellite orbits, distances to nearby larger halos, and the maximum circular velocity of the largest satellite galaxy. In this paper, we demonstrate a proof-of-concept method on simulated dark matter halos; in future papers in this series, we will apply neural networks to estimate the masses of the Milky Way’s and M31’s dark matter halos, and we will train variations of these networks to estimate other halo properties including concentration, assembly history, and spin axis.

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Authors
Hayati, E., Behroozi, P., & Patel, E.
Affiliations
  • University of Arizona (US)
  • University of Utah (US)
Venue
The Open Journal of Astrophysics
Published
2024-04-05
Volume
7
ISSN
2565-6120 (ISSN-L)
OpenAlex
W4393988296
Cited by
3
References
79
Citations by year
2026: 2 · 2025: 1
FWCI
0.57
Type
journal article
Language
en
Open access
Locations
  • The Open Journal of Astrophysics (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Science Foundation (1829740)
  • National Aeronautics and Space Administration (HST-HF2-51540.001-A, NAS 526555)
  • Gordon and Betty Moore Foundation
  • University of Arizona
  • Space Telescope Science Institute (#2019-69646, NAS5- 26555, # HST-HF2-51540.001-A, 26555)
  • Partnership for Advanced Computing in Europe AISBL
  • Leibniz-Gemeinschaft
  • Leibniz-Rechenzentrum
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
Keywords
halo mass, Milky Way, Milky Way analogs, artificial neural networks, satellite galaxies, satellite orbits, halo concentration, halo assembly history, M31, halo mass estimation

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