Constraining the SN Ia Host Galaxy Dust Law Distribution and Mass Step: Hierarchical BayeSN Analysis of Optical and Near-Infrared Light Curves
Stephen Thorp, Kaisey S. Mandel

TL;DR
This study employs hierarchical Bayesian modeling of optical and near-infrared light curves of Type Ia supernovae to better understand host galaxy dust properties and their impact on distance measurements, revealing a persistent mass step in residuals.
Contribution
It introduces a hierarchical Bayesian approach to accurately constrain the distribution of dust $R_V$ values and host mass effects, improving upon previous methods that overestimate population variance.
Findings
Estimated $R_V$ distribution with mean 2.59 and std 0.62 for low-reddening SNe.
No significant difference in $R_V$ distribution between low- and high-mass host galaxies.
A mass step of at least 0.06 mag remains in Hubble residuals after accounting for dust.
Abstract
We use the BayeSN hierarchical probabilistic SED model to analyse the optical-NIR () 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 population distribution by leveraging optical-NIR colour information. We demonstrate how a simplistic analysis where individual values are first estimated for each SN separately, and then the sample variance of these point estimates is computed, overestimates the population variance . This bias is exacerbated when neglecting residual intrinsic colour variation beyond that due to light curve shape. Instead, Bayesian shrinkage estimates of are more accurate, with fully hierarchical…
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Taxonomy
TopicsGamma-ray bursts and supernovae
