Inferring galaxy dark halo properties from visible matter with machine learning (original) (raw)

Monthly Notices of the Royal Astronomical Society

Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved nature of dark matter (DM). At galaxy scales, the density distribution of DM is strongly affected by feedback processes, which are difficult to fully account for in classical techniques to derive galaxy masses. We explore the capability of supervised machine learning (ML) algorithms to predict the DM content of galaxies from ‘luminous’ observational-like parameters, using the TNG100 simulation. In particular, we use photometric (magnitudes in different bands), structural (the stellar half-mass radius and three different baryonic masses), and kinematic (1D velocity dispersion and the maximum rotation velocity) parameters to predict the total DM mass, DM half-mass radius, and DM mass inside one and two stellar half-mass radii. We adopt the coefficient...