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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 $(800 ; mathrm{Mpc})^3$ P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the EAGLE model in this large volume.
We report the alignment and shape of dark matter, stellar, and hot gas distributions in the EAGLE and cosmo-OWLS simulations. The combination of these state-of-the-art hydro-cosmological simulations enables us to span four orders of magnitude in halo
We investigate the internal structure and density profiles of halos of mass $10^{10}-10^{14}~M_odot$ in the Evolution and Assembly of Galaxies and their Environment (EAGLE) simulations. These follow the formation of galaxies in a $Lambda$CDM Universe
We investigate the abundance of galactic molecular hydrogen (H$_2$) in the Evolution and Assembly of GaLaxies and their Environments (EAGLE) cosmological hydrodynamic simulations. We assign H$_2$ masses to gas particles in the simulations in post-pro
We use the EAGLE suite of hydrodynamical simulations to analyse accretion rates (and the breakdown of their constituent channels) onto haloes over cosmic time, comparing the behaviour of baryons and dark matter (DM). We also investigate the influence
We use the eagle simulations to study the connection between the quenching timescale, $tau_{rm Q}$, and the physical mechanisms that transform star-forming galaxies into passive galaxies. By quantifying $tau_{rm Q}$ in two complementary ways - as the