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.
Highlights
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M 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
Summary
A Cosmological hydrodynamic simulations self-consistently model the evolution of baryonic and cold dark matter, and the subsequent hierarchical assembly of galaxies in a ΛCDM. These use initial conditions selected with rank ordered galaxies from observations Such models from a much larger dark matter only (DMO) simulation, of order ∼ (1 Gpc) in volume, and resimulate a smaller have been used to constrain the stellar mass - halo mass relation Legrand et al 2019), though it has been noted that the scale tidal forces are preserved by simulating the rest of efficacy of such methods is highly dependent on the obthe volume with low resolution dark matter only particles This approach has been used successfully to simulate cluster these approaches are capable of modelling galaxy evolution environments with the EAGLE code (Barnes et al 2017b; Bahe et al 2017). In this paper we build on these previous works, by combining the results of both periodic and zoom cosmological simulations from the EAGLE project to train a machine
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