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An interpretable machine-learning framework for dark matter halo 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: 32019 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 490, no 1, p. 331-342Article in journal (Refereed) Published
Abstract [en]

We present a generalization of our recently proposed machine-learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range 11.4 <= log (M/M-circle dot) = 13.4. The algorithm is trained on an N-body simulation to infer the final mass of the halo to which each dark matter particle will later belong. We then quantify the difference in the predictive accuracy between machine-learning models using a metric based on the Kullback-Leibler divergence. We first train the algorithm with information about the density contrast in the particles' local environment. The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model. This result is confirmed as we verify the ability of the initial conditions-to-halo mass mapping learnt from one simulation to generalize to independent simulations. Our work illustrates the broader potential of developing interpretable machine-learning frameworks to gain physical understanding of non-linear large-scale structure formation.

Place, publisher, year, edition, pages
2019. Vol. 490, no 1, p. 331-342
Keywords [en]
methods: statistical, galaxies: haloes, dark matter, large-scale structure of Universe
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-176581DOI: 10.1093/mnras/stz2599ISI: 000496922300025OAI: oai:DiVA.org:su-176581DiVA, id: diva2:1377353
Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-11Bibliographically approved

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Peiris, Hiranya
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