Using Artificial Intelligence and real galaxy images to constrain parameters in galaxy formation simulations

TL;DR

This paper demonstrates that machine learning applied to galaxy images can effectively constrain key parameters in galaxy formation simulations, improving the understanding of galaxy evolution and dark matter distribution.

Contribution

It introduces a novel method using AI to analyze galaxy images, enabling direct comparison of simulations and observations without relying on simplified metrics.

Findings

High star formation density threshold n ≳ 80 cm⁻³ is supported by observations.

Method effectively distinguishes between different simulation parameters.

Full image information enhances the interpretability of galaxy simulation comparisons.

Abstract

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.