FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology

Abstract

Precision cosmology is a cornerstone of modern astronomy, and upcoming wide-field photometric surveys make it crucial to develop robust, data-driven inference methods that can handle complex survey systematics without relying on closed-form likelihoods. Simulation-Based Inference (SBI) meets this need by enabling forward simulations that encode complex survey characteristics. Previous SBI analyses in supernova cosmology have used SALT2 light-curve parameters as summary statistics; however, SALT2's two-basis spectral template imposes rigid modeling assumptions, motivating a more flexible representation. In this work, we present the first application of Functional Principal Component Analysis (FPCA) light-curve parameters as summary statistics for SBI-based cosmological inference under a non-flat ΛCDM model. On identically generated simulations, FPCA+SBI yields constraints comparable to both SALT2-based SBI and explicit-likelihood analyses, while providing more robust constraints on out-of-domain simulations than SALT2. Applying the model trained on LSST-like light curves to a spectroscopically confirmed DES Year 5 supernova sample, we recover constraints consistent with those of the DES collaboration to within 0.12 σ and 0.23 σ in Ωm and ΩΛ respectively, establishing the method's generalizability to real survey data. By introducing host-dependent systematics through a mass-dependent dust extinction law, we find FPCA-based summary statistics implicitly encode host-dependent systematics, suggesting dedicated host modeling may be unnecessary within this framework. Combined with its previously demonstrated effectiveness for photometric classification, FPCA offers a compelling foundation for unified, data-driven pipelines capable of jointly performing supernova classification and cosmological inference for upcoming wide-field photometric surveys.