Bird-Snack: Bayesian inference of dust law RV distributions using SN Ia apparent colours at peak

Abstract

ABSTRACT To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, RV, must be accurately constrained. We thus develop a computationally inexpensive pipeline, Bird-Snack, to rapidly infer dust population distributions from optical-near-infrared SN colours at peak brightness, and determine which analysis choices significantly impact the population mean RV inference, $\mu _{R_V}$. Our pipeline uses a 2D Gaussian process to measure peak BVriJH apparent magnitudes from SN light curves, and a hierarchical Bayesian model to simultaneously constrain population distributions of intrinsic and dust components. Fitting a low-to-moderate-reddening sample of 65 low-redshift SNe yields $\mu _{R_V}=2.61^{+0.38}_{-0.35}$, with $68~{{\ \rm per\ cent}}(95~{{\ \rm per\ cent}})$ posterior upper bounds on the population dispersion, $\sigma _{R_V}\lt 0.92(1.96)$. This result is robust to various analysis choices, including: the model for intrinsic colour variations, fitting the shape hyperparameter of a gamma dust extinction distribution, and cutting the sample based on the availability of data near peak. However, these choices may be important if statistical uncertainties are reduced. With larger near-future optical and near-infrared SN samples, Bird-Snack can be used to better constrain dust distributions, and investigate potential correlations with host galaxy properties. Bird-Snack is publicly available; the modular infrastructure facilitates rapid exploration of custom analysis choices, and quick fits to simulated data sets, for better interpretation of real-data inferences.