Measuring Galactic dark matter through unsupervised machine learning

Buckley, Matthew R.; Lim, Sung Hak; Putney, Eric; Shih, David

doi:10.1093/mnras/stad843

Title: Measuring Galactic dark matter through unsupervised machine learning

Journal Article · 22 March 2023 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stad843 · OSTI ID:1968323

;

; Putney, Eric; Shih, David

ABSTRACT Measuring the density profile of dark matter in the Solar neighbourhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky Way-type galaxy to learn – in an unsupervised way – the stellar phase space density and its derivatives. With these as inputs, and under the assumption of dynamic equilibrium, the gravitational acceleration field and mass density can be calculated directly from the Boltzmann equation without the need to assume either cylindrical symmetry or specific functional forms for the galaxy’s mass density. We demonstrate our approach can accurately reconstruct the mass density and acceleration profiles of the simulated galaxy, even in the presence of Gaia-like errors in the kinematic measurements.

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Research Organization:: Rutgers Univ., Piscataway, NJ (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0010008

OSTI ID:: 1968323

Journal Information:: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 4 Vol. 521; ISSN 0035-8711

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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