Inferring galaxy dark halo properties from visible matter with machine learning

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 of determination, R-2, as a metric to evaluate the accuracy of these predictions. We find that using all observational quantities together (photometry, structural, and kinematics), we reach high accuracy for all DM quantities (up to R-2 similar to 0.98). This first test shows that ML tools are promising to predict the DM in real galaxies. The next steps will be to implement the observational realism of the training sets, by closely selecting samples that accurately reproduce the typical observed 'luminous' scaling relations. The so-trained pipelines will be suitable for real galaxy data collected from Rubin/Large Synoptic Survey Telescope (LSST), Euclid, Chinese Survey Space Telescope (CSST), 4-metre Multi-Object Spectrograph Telescope (4MOST), Dark Energy Spectroscopic Instrument (DESI), to derive e.g. the properties of their central DM fractions.