A COMPASS to Model Comparison and Simulation-Based Inference in Galactic Chemical Evolution
This enables principled model selection and parameter estimation for astrophysical simulators, addressing uncertainties in Galactic Chemical Evolution for researchers in astrophysics, though it is incremental as it applies existing SBI methods to a specific domain.
The authors tackled the problem of comparing Galactic Chemical Evolution models using high-dimensional stellar abundance data, and their COMPASS framework strongly favored specific nucleosynthetic yield combinations with near-unity posterior probability, inferring parameters like a steep IMF slope and elevated Supernova Ia normalization.
We present 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. COMPASS handles high-dimensional, incomplete, and variable-size stellar abundance datasets. Applied to high-precision elemental abundance measurements, 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.