CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry

• URL: http://arxiv.org/abs/2508.15899v1
• Date: Thu, 21 Aug 2025 18:00:29 GMT
• Title: CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry
• Authors: Konstantin Karchev, Roberto Trotta, Raul Jimenez,
• Abstract summary: We present a unified Bayesian hierarchical model designed to infer, from purely photometric observations, the intrinsic dependence of SN Ia brightness on progenitor properties.<n>We show with simulations that intrinsic dependences on metallicity and age have distinct observational signatures.<n>Our joint physics-based approach delivers robust and precise photometric redshifts and improved cosmological constraints.
• Score: 1.9116784879310027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using type Ia supernovae (SNae Ia) as cosmological probes requires empirical corrections, which correlate with their host environment. We present a unified Bayesian hierarchical model designed to infer, from purely photometric observations, the intrinsic dependence of SN Ia brightness on progenitor properties (metallicity & age), the delay-time distribution (DTD) that governs their rate as a function of age, and cosmology, as well as the redshifts of all hosts. The model incorporates physics-based prescriptions for star formation and chemical evolution from Prospector-beta, dust extinction of both galaxy and SN light, and observational selection effects. We show with simulations that intrinsic dependences on metallicity and age have distinct observational signatures, with metallicity mimicking the well-known step of SN Ia magnitudes across a host stellar mass of $\approx 10^{10} M_{\odot}$. We then demonstrate neural simulation-based inference of all model parameters from mock observations of ~16 000 SNae Ia and their hosts up to redshift 0.9. Our joint physics-based approach delivers robust and precise photometric redshifts (<0.01 median scatter) and improved cosmological constraints, unlocking the full power of photometric data and paving the way for an end-to-end simulation-based analysis pipeline in the LSST era.

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