A Bayesian approach to truncated data sets: An application to Malmquist bias in Supernova Cosmology

TL;DR

This paper introduces a Bayesian hierarchical method to correct for Malmquist bias in supernova cosmology data, improving parameter inference from incomplete magnitude-limited surveys.

Contribution

It develops a novel Bayesian hierarchical framework specifically designed to handle magnitude-limited survey data in supernova cosmology.

Findings

Effective correction for Malmquist bias demonstrated

Enhanced accuracy in cosmological parameter estimation

Applicable to large, incomplete astronomical data sets

Abstract

Large scale astronomical surveys are going wider and deeper than ever before. However, astronomers, cosmologists and theorists continue to face the perennial issue that their data sets are often incomplete in magnitude space and must be carefully treated in order to avoid Malmquist bias, especially in the field of supernova cosmology. Historically, cosmological parameter inference in supernova cosmology was done using $\chi^2$ methodology; however, recent years have seen a rise in the use of Bayesian Hierarchical Models. In this paper we develop a Bayesian Hierarchical methodology to account for magnitude limited surveys and present a specific application to cosmological parameter inference and model selection in supernova cosmology.