Variational Inference for Acceleration of SN Ia Photometric Distance Estimation with BayeSN

TL;DR

This paper introduces a variational inference approach to efficiently estimate distances of Type Ia supernovae, significantly speeding up analysis while maintaining accuracy, thus facilitating larger cosmological datasets.

Contribution

The paper presents the first application of variational inference to SN Ia light curve modeling within the BayeSN framework, achieving faster inference with comparable accuracy to MCMC methods.

Findings

VI achieves similar results to MCMC in distance estimation.

VI provides an order-of-magnitude speedup in inference.

Validation confirms VI's reliability for large datasets.

Abstract

Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new datasets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions (SEDs). We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior -- a multivariate normal and a custom multivariate zero-lower-truncated normal distribution -- and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the pareto-smoothed importance sampling (PSIS) diagnostic, and perform variational simulation-based calibration (VSBC). The VI approximation achieves similar results to MCMC but with an order-of-magnitude speedup for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger datasets for precision cosmology.