2022MNRAS.517.2360T

We use the BAYESN hierarchical probabilistic SED model to analyse the optical-NIR (BVriYJH) light curves of 86 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project to investigate the SN Ia host galaxy dust law distribution and correlations between SN Ia Hubble residuals and host mass. Our Bayesian analysis simultaneously constrains the mass step and dust R_V population distribution by leveraging optical-NIR colour information. We demonstrate how a simplistic analysis where individual R_V values are first estimated for each SN separately, and then the sample variance of these point estimates is computed, overestimates the R_V population variance $\sigma _R^2$. This bias is exacerbated when neglecting residual intrinsic colour variation beyond that due to light curve shape. Instead, Bayesian shrinkage estimates of σ_R are more accurate, with fully hierarchical analysis of the light curves being ideal. For the 75 SNe with low-to-moderate reddening (peak apparent B - V ≤ 0.3), we estimate an R_V distribution with population mean μ_R = 2.59 ± 0.14, and standard deviation σ_R = 0.62 ± 0.16. Splitting this subsample at the median host galaxy mass (10^10.57 M_⊙) yields consistent estimated R_V distributions between low- and high-mass galaxies, with μ_R = 2.79 ± 0.18, σ_R = 0.42 ± 0.24, and μ_R = 2.35 ± 0.27, σ_R = 0.74 ± 0.36, respectively. When estimating distances from the full optical-NIR light curves while marginalizing over various forms of the dust R_V distribution, a mass step of ≳0.06 mag persists in the Hubble residuals at the median host mass.