UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework

TL;DR

This paper introduces UNITY, a Bayesian framework for supernova cosmology that improves the treatment of uncertainties, outliers, and standardization relations, leading to more precise and reliable cosmological measurements.

Contribution

UNITY is a novel Bayesian approach that addresses limitations of current supernova analyses by incorporating nonlinear standardizations and systematic effects in a statistically rigorous manner.

Findings

Reduced statistical and systematic uncertainties in supernova measurements

Confirmed the necessity of nonlinear shape and color standardizations

Validated the framework on simulated and real data

Abstract

While recent supernova cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current supernova cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real supernova observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.