Evaluating the Realism of Sparse Galaxy Simulations Using Out-of-Distribution Detection and Bayesian Model Comparison Against SDSS Observations

This research leverages machine learning techniques, specifically out-of-distribution detection and amortized Bayesian model comparison, to evaluate the realism of six different hydrodynamical galaxy simulations by comparing them to real observational data from the Sloan Digital Sky Survey (SDSS).