SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation

TL;DR

This paper introduces a likelihood-free inference method using truncated marginal neural ratio estimation to efficiently analyze large supernova Ia datasets for cosmology, accounting for complex uncertainties and latent parameters.

Contribution

It demonstrates the application of TMNRE for unbiased, precise cosmological parameter inference from large supernova datasets, enabling simultaneous latent parameter estimation and improved modeling.

Findings

Unbiased and precise posteriors for cosmological parameters from 100,000 supernovae.

Efficient inference of over 100,000 latent supernova parameters.

Conversion of Bayesian posteriors into frequentist confidence regions with exact coverage.

Abstract

Type Ia supernovae (SNae Ia), standardisable candles that allow tracing the expansion history of the Universe, are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses scale poorly to future large datasets, are limited to simplified probabilistic descriptions, and must explicitly sample a high-dimensional latent posterior to infer the few parameters of interest, which makes them inefficient. Marginal likelihood-free inference, on the other hand, is based on forward simulations of data, and thus can fully account for complicated redshift uncertainties, contamination from non-SN Ia sources, selection effects, and a realistic instrumental model. All latent parameters, including instrumental and survey-related ones, per-object and population-level properties, are implicitly marginalised, while the cosmological parameters of interest are inferred directly. As a proof of concept, we apply truncated marginal neural ratio estimation (TMNRE), a form of marginal likelihood-free inference, to BAHAMAS, a Bayesian hierarchical model for SALT parameters. We verify that TMNRE produces unbiased and precise posteriors for cosmological parameters from up to 100 000 SNae Ia. With minimal additional effort, we train a network to infer simultaneously the O(100 000) latent parameters of the supernovae (e.g. absolute brightnesses). In addition, we describe and apply a procedure that utilises local amortisation of the inference to convert the approximate Bayesian posteriors into frequentist confidence regions with exact coverage. Finally, we discuss the planned improvements to the model that are enabled by using a likelihood-free inference framework, like selection effects and non-Ia contamination.