A machine learning approach to mapping baryons on to dark matter haloes using the eagle and C-EAGLE simulations

Monthly Notices of the Royal Astronomical Society, vol. 509, pp. 5046–5061

Abstract

ABSTRACT High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure clustering statistics of the large-scale structure. Typically, zoom simulations of individual regions are used to study rare environments, and semi-analytic models and halo occupation models applied to dark-matter-only (DMO) simulations are used to study the Universe in the large-volume regime. We propose a new approach, using a machine learning framework, to explore the halo–galaxy relationship in the periodic eagle simulations, and zoom C-EAGLE simulations of galaxy clusters. We train a tree-based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. The trained model successfully reproduces a number of key distribution functions for an infinitesimal fraction of the computational cost of a full hydrodynamic simulation. By training on both periodic simulations and zooms of overdense environments, we learn the bias of galaxy evolution in differing environments. This allows us to apply the trained model to a larger DMO volume than would be possible if we only trained on a periodic simulation. We demonstrate this application using the (800 Mpc)3 P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the eagle model in this large volume.

Authors 6

1. Christopher C. Lovell corresponding

   University of Hertfordshire

   Affiliation as printed

   Centre for Astrophysics Research, School of Physics, Astronomy & Mathematics, University of Hertfordshire , Hatfield AL10 9AB, UK

2. Stephen M. Wilkins corresponding

   University of Sussex

   Affiliation as printed

   Astronomy Centre, Department of Physics and Astronomy, University of Sussex , Brighton BN1 9QH, UK

3. Matthieu Schaller Aachen Leiden Observatory Lorentz Institute for Theoretical Physics

   Leiden University · Leiden Observatory

   Affiliation as printed

   Leiden Observatory, Leiden University , PO Box 9513, NL-2300 RA Leiden, the Netherlands

   Lorentz Institute for Theoretical Physics, Leiden University , PO Box 9506, NL-2300 RA Leiden, the Netherlands

4. C. M. Baugh

   Durham University

   Affiliation as printed

   Institute for Computational Cosmology, Department of Physics, Durham University, Science Laboratories , South Road, Durham DH1 3LE, UK

5. Giulio Fabbian

   Flatiron Institute · Cardiff University

   Affiliation as printed

   Center for Computational Astrophysics, Flatiron Institute , New York, NY 10010, USA

   School of Physics and Astronomy, Cardiff University, The Parade , Cardiff CF24 3AA, UK

6. Yannick M Bahé Aachen Leiden Observatory

   Leiden University · Leiden Observatory

   Affiliation as printed

   Leiden Observatory, Leiden University , PO Box 9513, NL-2300 RA Leiden, the Netherlands