2022MNRAS.512.2135M

Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they still rely on a set of 'effective parameters' that try to capture the scales and the physical processes that cannot be directly resolved in the simulation. In this study, we show that it is possible to use Machine Learning techniques applied to real and simulated images of galaxies to discriminate between different values of these parameters by making use of the full information content of an astronomical image instead of collapsing it into a limited set of values like size, or stellar/ gas masses. In this work, we apply our method to the NIHAO simulations and the THINGS and VLA-ANGST observations of H I maps in nearby galaxies to test the ability of different values of the star formation density threshold n to reproduce observed H I maps. We show that observations indicate the need for a high value of n >= 80 cm^–3 (although the numerical value is model-dependent), which has important consequences for the dark matter distribution in galaxies. Our study shows that with innovative methods it is possible to take full advantage of the information content of galaxy images and compare simulations and observations in an interpretable, non-parametric, and quantitative manner.