Improved Cosmological Constraints from a Bayesian Hierarchical Model of Supernova Type Ia Data

摘要: We present a Bayesian hierarchical model for inferring the cosmological parameters from supernovae type Ia fitted with SALT-II lightcurve fitter. demonstrate simulated data sets that our method delivers tighter statistical constraints on over 90% of time, it reduces bias typically by factor ~2–3 and has better coverage properties than usual χ 2 approach. As further benefit, full posterior probability distribution dispersion intrinsic magnitude SNe is obtained. apply this to recent SNIa data, combining them CMB BAO we obtain Ωm = 0:28 ± 0:02, ΩΛ 0:73 0:01 (assuming ω −1) Ω m 0:01, −0:90 0:05 flatness; uncertainties only). constrain B-band population, obtaining \(\sigma _\mu ^{\text{int}} \) 0:13 [mag].