BayeSN and SALT: A Comparison of Dust Inference Across SN Ia Light-curve Models with DES5YR

Abstract

In recent years there has been significant debate around the impact of dust on SNe Ia, a major source of uncertainty in cosmological analyses. We perform the first validation of the probabilistic hierarchical SN Ia SED model BayeSN on the conventional SALT model, an important test given the history of conflicting conclusions regarding the distributions of host galaxy dust properties between the two. Applying BayeSN to SALT-based simulations, we find that BayeSN is able to accurately recover our simulated inputs and successfully disentangle differences in dust extinction from an intrinsic mass step. This validates BayeSN as a method to identify the relative contributions of dust and intrinsic differences in explaining the mass step. When inferring dust parameters with simulated samples including non-Ia contamination, we find that our choice of photometric classifier causes a bias in the inferred dust distribution; this arises because SNe Ia heavily impacted by dust are misclassified as contaminants and excluded. We then apply BayeSN to the sample of SNe from DES5YR to jointly infer host galaxy dust distributions and intrinsic differences on either side of the `mass step' at $10^{10}$ M$\odot$. We find evidence in favour of an intrinsic contribution to the mass step and differing $R_V$ distributions. We also build on recent results supporting an environmental-dependence on the secondary maximum of SNe Ia in $i$-band. Twenty days post-peak, we find an offset in intrinsic $i$-band light curve between each mass bin at a significance in excess of $3\sigma$.