Machine Learning methods to estimate observational properties of galaxy
  clusters in large volume cosmological N-body simulations
  In this paper we study the applicability of a set of supervised machine
learning (ML) models specifically trained to infer observed related properties
of the baryonic component (stars and gas) from a set of features of dark matter
only cluster-size halos. The training set is built from THE THREE HUNDRED
project which consists of a series of zoomed hydrodynamical simulations of
cluster-size regions extracted from the 1 Gpc volume MultiDark dark-matter only
simulation (MDPL2). We use as target variables a set of baryonic properties for
the intra cluster gas and stars derived from the hydrodynamical simulations and
correlate them with the properties of the dark matter halos from the MDPL2
N-body simulation. The different ML models are trained from this database and
subsequently used to infer the same baryonic properties for the whole range of
cluster-size halos identified in the MDPL2. We also test the robustness of the
predictions of the models against mass resolution of the dark matter halos and
conclude that their inferred baryonic properties are rather insensitive to
their DM properties which are resolved with almost an order of magnitude
smaller number of particles. We conclude that the ML models presented in this
paper can be used as an accurate and computationally efficient tool for
populating cluster-size halos with observational related baryonic properties in
large volume N-body simulations making them more valuable for comparison with
full sky galaxy cluster surveys at different wavelengths. We make the best ML
trained model publicly available.
