Berkay Gunes, Sven Buder, Tobias Buck

We present \texttt{COMPASS}, a novel simulation-based inference framework that combines score-based diffusion models with transformer architectures to jointly perform parameter estimation and Bayesian model comparison across competing Galactic Chemical Evolution (GCE) models. \texttt{COMPASS} handles high-dimensional, incomplete, and variable-size stellar abundance datasets. % Applied to high-precision elemental abundance measurements, \texttt{COMPASS} evaluates 40 combinations of nucleosynthetic yield tables. The model strongly favours Asymptotic Giant Branch yields from NuGrid and core-collapse SN yields used in the IllustrisTNG simulation, achieving near-unity cumulative posterior probability. Using the preferred model, we infer a steep high-mass IMF slope and an elevated Supernova\,Ia normalization, consistent with prior solar neighbourhood studies but now derived from fully amortized Bayesian inference. % Our results demonstrate that modern SBI methods can robustly constrain uncertain physics in astrophysical simulators and enable principled model selection when analysing complex, simulation-based data.