Lucie-Smith, Luisa;
Peiris, Hiranya;
Pontzen, Andrew;
(2024)
Explaining Dark Matter Halo Density Profiles with Neural Networks.
Physical Review Letters
, 132
(3)
, Article 031001. 10.1103/PhysRevLett.132.031001.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Explaining Dark Matter Halo Density Profiles with Neural Networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1103/PhysRevLett.132.031001 |
| Publisher version: | https://doi.org/10.1103/PhysRevLett.132.031001 |
| Language: | English |
| Additional information: | © The Author(s), 2024. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Open access publication funded by the Max Planck Society. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Dark matter, Large scale structure of the Universe, Astrophysical & cosmological simulations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10191080 |
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