BEAMS: separating the wheat from the chaff in supernova analysis
Instrumentation and Methods for Astrophysics
2016-12-07 v1 Cosmology and Nongalactic Astrophysics
Data Analysis, Statistics and Probability
Applications
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.
Cite
@article{arxiv.1210.7762,
title = {BEAMS: separating the wheat from the chaff in supernova analysis},
author = {Martin Kunz and Renée Hlozek and Bruce A. Bassett and Mathew Smith and James Newling and Melvin Varughese},
journal= {arXiv preprint arXiv:1210.7762},
year = {2016}
}
Comments
23 pages, 9 figures. Chapter 4 in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics