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BEAMS: separating the wheat from the chaff in supernova analysis

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arxiv 1210.7762 v1 pith:AJ24CRBS submitted 2012-10-29 astro-ph.IM astro-ph.COphysics.data-anstat.AP

Martin Kunz , Renée Hlozek , Bruce A. Bassett , Mathew Smith , James Newling , Melvin Varughese This is my paper · ORCID
classification astro-ph.IMastro-ph.COphysics.data-anstat.AP
keywords algorithmbeamsdataestimationsupernovaanalysisappliedapply
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BayeSN $\times$ Dovekie: Joint Photometric Cross-calibration and SED Modelling of Type Ia Supernovae

    astro-ph.CO 2026-06 unverdicted novelty 7.0 of 10

    Joint photometric cross-calibration and SED modeling in BayeSN yields G26 model with 12% NMAD scatter reduction on DES-SN5YR supernovae at z<0.7.

  2. A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    A simulation-based inference pipeline (Stjörnumál) fits SN Ia dust and intrinsic scatter models to DES 5-year data, enabling fast Bayesian model comparison across seven SN Ia population models.

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