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We present principled Bayesian model comparison through simulation-basedneural classification applied to SN Ia analysis. We validate our approach onrealistically simulated SN Ia light curve data, demonstrating its ability torecover posterior model probabilities while marginalizing over >4000 latentvariables. The amortized nature of our technique allows us to explore thedependence of Bayes factors on the true parameters of simulated data,demonstrating Occam's razor for nested models. When applied to a sample of 86low-redshift SNae Ia from the Carnegie Supernova Project, our method prefers amodel with a single dust law and no magnitude step with host mass, disfavouringdifferent dust laws for low- and high-mass hosts with odds in excess of 100:1.

SimSIMS: Simulation-based Supernova Ia Model Selection with thousands of latent variables