Machine learning within the THREEHUNDRED simulation project (Daniel De Andres)

Contributed presentation at 2021 IAP conference “Debating the potential of machine learning in astronomical surveys“ Abstract: The THREEHUNDRED project aims at collecting one of the largest set of hydrodynamical simulations of massive galaxy clusters. Since massive clusters are very scarce, the zooming technique is used to individually resimulate spherical regions around the 324 most massive objects found in the MULTIDARK, 1 giga-parsec volume N-body only simulation. From the results of these simulations we train a machine learning algorithm to find a mapping between dark matter halo properties derived by Rockstar halo finder and baryon properties, such us gas mass , stellar mass and the Compton Y parameter within R500 obtained from the hydrodynamical simulations. By doing so, we successfully populate the whole MultiDark cluster-size halos with baryonic properties similar to those from the THREEHUNDRED hydro simulations. We also prove that the trained models can also be applied to other N-body simulations ev