BEAMS: separating the wheat from the chaff in supernova analysis

TL;DR

BEAMS is a Bayesian algorithm that effectively handles contaminated data in supernova analysis, improving parameter estimation accuracy and tightening confidence intervals in cosmological studies.

Contribution

This paper introduces BEAMS, a novel Bayesian method for analyzing contaminated data sets, demonstrated with simulations and real SDSS supernova data.

Findings

Confidence contours of cosmological parameters shrink significantly.

BEAMS outperforms traditional methods in contaminated data scenarios.

Effective in both simulated and real supernova datasets.

Abstract

We introduce Bayesian Estimation Applied to Multiple Species (BEAMS), an algorithm designed to deal with parameter estimation when using contaminated data. We present the algorithm and demonstrate how it works with the help of a Gaussian simulation. We then apply it to supernova data from the Sloan Digital Sky Survey (SDSS), showing how the resulting confidence contours of the cosmological parameters shrink significantly.