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

Lovell, Christopher C; Wilkins, Stephen M; Thomas, Peter A; Schaller, Matthieu; Baugh, Carlton M; Fabbian, Giulio; Bahé, Yannick

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 as well as 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.

Citation

Lovell, C. C., Wilkins, S. M., Thomas, P. A., Schaller, M., Baugh, C. M., Fabbian, G., & Bahé, Y. (2022). 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, 509(4), 5046-5061. https://doi.org/10.1093/mnras/stab3221

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2021
• Online Publication Date: Nov 11, 2021
• Publication Date: 2022-02
• Deposit Date: Dec 8, 2021
• Publicly Available Date: Dec 8, 2021
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Royal Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 509
• Issue: 4
• Pages: 5046-5061
• DOI: https://doi.org/10.1093/mnras/stab3221
• Public URL: https://durham-repository.worktribe.com/output/1218983