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Machine learning cosmological structure formation
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). University College London, UK.
Number of Authors: 42018 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 479, no 3, p. 3405-3414Article in journal (Refereed) Published
Abstract [en]

We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo collapse models. We gain insights into the physics driving halo formation by evaluating the predictive performance of the algorithm when provided with different types of information about the local environment around dark matter particles. The algorithm learns to predict whether or not dark matter particles will end up in haloes of a given mass range, based on spherical overdensities. We show that the resulting predictions match those of spherical collapse approximations such as extended Press-Schechter theory. Additional information on the shape of the local gravitational potential is not able to improve halo collapse predictions; the linear density field contains sufficient information for the algorithm to also reproduce ellipsoidal collapse predictions based on the Sheth-Tormen model. We investigate the algorithm's performance in terms of halo mass and radial position and perform blind analyses on independent initial conditions realizations to demonstrate the generality of our results.

Place, publisher, year, edition, pages
2018. Vol. 479, no 3, p. 3405-3414
Keywords [en]
methods: statistical, galaxies: haloes, dark matter, large-scale structure of Universe
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-160237DOI: 10.1093/mnras/sty1719ISI: 000441382300041OAI: oai:DiVA.org:su-160237DiVA, id: diva2:1249851
Available from: 2018-09-20 Created: 2018-09-20 Last updated: 2018-09-20Bibliographically approved

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Peiris, Hiranya V.
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