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.
BEAMS: separating the wheat from the chaff in supernova analysis
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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.
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astro-ph.CO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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BayeSN $\times$ Dovekie: Joint Photometric Cross-calibration and SED Modelling of Type Ia Supernovae
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.