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

Understand

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.