publication . Article . Preprint . Other literature type . 2019

An interpretable machine-learning framework for dark matter halo formation

Lucie-Smith, Luisa; Peiris, Hiranya V; Pontzen, Andrew;
Open Access
  • Published: 16 Sep 2019 Journal: Monthly Notices of the Royal Astronomical Society, volume 490, pages 331-342 (issn: 0035-8711, eissn: 1365-2966, Copyright policy)
  • Publisher: Oxford University Press (OUP)
  • Country: United Kingdom
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 $11.4 \leq \log(M/M_{\odot}) \leq 13.4$. 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 contr...
arXiv: Astrophysics::Cosmology and Extragalactic AstrophysicsAstrophysics::Galaxy Astrophysics
free text keywords: Space and Planetary Science, Astronomy and Astrophysics, methods: statistical, galaxies: haloes, dark matter, large-scale structure of Universe, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Funded by
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
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