Machine Learning the Dark Matter Halo Mass of the Milky Way
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arXiv
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| Format: | Preprint |
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2025
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| author | Hayati, Elaheh Behroozi, Peter Patel, Ekta Wang, Yunchong Gottlöber, Stefan Yepes, Gustavo |
| author_facet | Hayati, Elaheh Behroozi, Peter Patel, Ekta Wang, Yunchong Gottlöber, Stefan Yepes, Gustavo |
| contents | Although numerous dynamical techniques have been developed to estimate the total dark matter halo mass of the Milky Way, it remains poorly constrained, with typical systematic uncertainties of 0.3 dex. In this study, we apply a neural network-based approach that achieves high mass precision without several limitations that have affected past approaches; for example, we do not assume dynamical equilibrium, nor do we assume that neighboring galaxies are bound satellites. Additionally, this method works for a broad mass range, including for halos that differ significantly from the Milky Way. Our model relies solely on observable dynamical quantities, such as satellite orbits, distances to larger nearby halos, and the maximum circular velocity of the most massive satellite. In this paper, we measure the halo mass of the Milky Way to be log_10 M_vir / M_Sun = 12.20^{+0.163}_{-0.138}. Future studies in this series will extend this methodology to estimate the dark matter halo mass of M31, and develop new neural networks to infer additional halo properties including concentration, assembly history, and spin axis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning the Dark Matter Halo Mass of the Milky Way Hayati, Elaheh Behroozi, Peter Patel, Ekta Wang, Yunchong Gottlöber, Stefan Yepes, Gustavo Astrophysics of Galaxies Although numerous dynamical techniques have been developed to estimate the total dark matter halo mass of the Milky Way, it remains poorly constrained, with typical systematic uncertainties of 0.3 dex. In this study, we apply a neural network-based approach that achieves high mass precision without several limitations that have affected past approaches; for example, we do not assume dynamical equilibrium, nor do we assume that neighboring galaxies are bound satellites. Additionally, this method works for a broad mass range, including for halos that differ significantly from the Milky Way. Our model relies solely on observable dynamical quantities, such as satellite orbits, distances to larger nearby halos, and the maximum circular velocity of the most massive satellite. In this paper, we measure the halo mass of the Milky Way to be log_10 M_vir / M_Sun = 12.20^{+0.163}_{-0.138}. Future studies in this series will extend this methodology to estimate the dark matter halo mass of M31, and develop new neural networks to infer additional halo properties including concentration, assembly history, and spin axis. |
| title | Machine Learning the Dark Matter Halo Mass of the Milky Way |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2509.25362 |