Improved cosmological constraints from a Bayesian hierarchical model of supernova type Ia data

We present a Bayesian hierarchical model for inferring the cosmological parameters from the supernovae type Ia fitted with the SALT-II lightcurve fitter. We demonstrate with simulated data sets that our method delivers tighter statistical constraints on the cosmological parameters over 90% of the time, that it reduces statistical bias typically by a factor ~2–3 and that it has better coverage properties than the usual χ2 approach. As a further benefit, a full posterior probability distribution for the dispersion of the intrinsic magnitude of SNe is obtained. We apply this method to recent SNIa data, and by combining them with CMB and BAO data we obtain Ωm = 0:28 ± 0:02, ΩΛ = 0:73 ± 0:01 (assuming ω = −1) and Ωm = 0:28 ± 0:01, ω = −0:90 ± 0:05 (assuming flatness; statistical uncertainties only). We constrain the intrinsic dispersion of the B-band magnitude of the SNIa population, obtaining (Formula Presented) = 0:13 ± 0:01 [mag]. © Springer Science+Business Media New York 2013.