Towards the Future of Supernova Cosmology

TL;DR

This paper discusses the challenges of supernova classification in future surveys and introduces BEAMS, a Bayesian method that effectively uses all data for unbiased cosmological parameter estimation, even with correlated data.

Contribution

The paper provides a pedagogical overview of BEAMS, demonstrating its effectiveness with simulated and SDSS-II data, and extends it to correlated datasets through numerical marginalisation.

Findings

BEAMS yields unbiased cosmological parameters with full datasets.

It can handle correlated data via numerical marginalisation.

Effective with both simulated and real SDSS-II data.

Abstract

For future surveys, spectroscopic follow-up for all supernovae will be extremely difficult. However, one can use light curve fitters, to obtain the probability that an object is a Type Ia. One may consider applying a probability cut to the data, but we show that the resulting non-Ia contamination can lead to biases in the estimation of cosmological parameters. A different method, which allows the use of the full dataset and results in unbiased cosmological parameter estimation, is Bayesian Estimation Applied to Multiple Species (BEAMS). BEAMS is a Bayesian approach to the problem which includes the uncertainty in the types in the evaluation of the posterior. Here we outline the theory of BEAMS and demonstrate its effectiveness using both simulated datasets and SDSS-II data. We also show that it is possible to use BEAMS if the data are correlated, by introducing a numerical marginalisation over the types of the objects. This is largely a pedagogical introduction to BEAMS with references to the main BEAMS papers.