  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 HI maps in nearby galaxies to test the ability of different
values of the star formation density threshold $n$ to reproduce observed HI
maps. We show that observations indicate the need for a high value of $n
\gtrsim 80$ ,cm$^{-3}$ (although the exact 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.
