Lucie -Smith, Luisa;
Peiris, Hiranya V;
Pontzen, Andrew;
Nord, Brian;
Thiyagalingam, Jeyan;
(2024)
Deep learning insights into cosmological structure formation.
Physical Review D
, 109
(6)
, Article 063524. 10.1103/PhysRevD.109.063524.
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Abstract
The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation.
| Type: | Article |
|---|---|
| Title: | Deep learning insights into cosmological structure formation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1103/PhysRevD.109.063524 |
| Publisher version: | https://doi.org/10.1103/PhysRevD.109.063524 |
| 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. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Dark matter, Large scale structure of the Universe, Astrophysical & cosmological simulations, Numerical simulations in gravitation & astrophysics |
| 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/10191079 |
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