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

2025-08-21

AI Summary

This study aims to jointly infer the intrinsic luminosity dependence of Type Ia supernovae (SNe Ia) on progenitor metallicity and age, their delay-time distribution (DTD), cosmological parameters, and host-galaxy redshift—using only photometric data of SNe Ia and their host galaxies, without spectroscopic information. Method: We construct a unified Bayesian hierarchical model integrating the Prospector-beta stellar population synthesis and chemical evolution framework, a galaxy–supernova extinction model, and observational selection effects. Crucially, we introduce, for the first time, an end-to-end neural simulation-based inference framework to fully couple galaxy astrophysical modeling with cosmological inference. Results: On simulated data, the method achieves a median photometric redshift bias < 0.01 and significantly tightens constraints on cosmological parameters. It establishes a novel, self-consistent analysis paradigm for next-generation photometric surveys—particularly for the LSST era—enabling full-chain, spectroscopy-free cosmological inference from large-scale transient catalogs.

Technology Category

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Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Infer SN Ia brightness dependence on progenitor metallicity and age
• Determine delay-time distribution and cosmology from photometric data
• Estimate redshifts of all host galaxies using unified Bayesian model

Innovation

• Bayesian hierarchical model for supernova analysis
• Neural simulation-based inference from photometry
• Joint physics-based approach for redshifts