Explaining dark matter halo density profiles with neural networks
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866909076946944000 |
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| author | Lucie-Smith, Luisa Peiris, Hiranya V. Pontzen, Andrew |
| author_facet | Lucie-Smith, Luisa Peiris, Hiranya V. Pontzen, Andrew |
| contents | We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network recovers the known relation between the early time assembly and the inner profile, and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03077 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Explaining dark matter halo density profiles with neural networks Lucie-Smith, Luisa Peiris, Hiranya V. Pontzen, Andrew Cosmology and Nongalactic Astrophysics Machine Learning We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network recovers the known relation between the early time assembly and the inner profile, and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets. |
| title | Explaining dark matter halo density profiles with neural networks |
| topic | Cosmology and Nongalactic Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2305.03077 |