BEAMS: separating the wheat from the chaff in supernova analysis

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.

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

References in corpus (6)

• Improved Distances to Type Ia Supernovae with Multicolor Light Curve Shapes: MLCS2k2
• The Sloan Digital Sky Survey-II Supernova Survey: Technical Summary
• The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations
• Results from the Supernova Photometric Classification Challenge
• Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae
• Type IIn supernovae at z ~ 2 from archival data

Cited by in corpus (4)

• The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset
• The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties
• Probing the Consistency of Cosmological Contours for Supernova Cosmology
• BayeSN and SALT: A Comparison of Dust Inference Across SN Ia Light-curve Models with DES5YR