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 (DM)-only cluster-size haloes. The training set is built from the three hundred project that consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume MultiDark DM-only simulation (MDPL2). We use as target variables a set of baryonic properties for the intracluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the DM haloes from the MDPL2 N-body simulation. The different ML models are trained from this data base and subsequently used to infer the same baryonic properties for the whole range of cluster-size haloes identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the DM haloes and conclude that their inferred baryonic properties are rather insensitive to their DM properties that 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 haloes 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.