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

• Published in: Monthly notices of the Royal Astronomical Society Vol. 526; no. 4; pp. 5715 - 5734
• Main Authors: Ward, Sam M, Dhawan, Suhail, Mandel, Kaisey S, Grayling, Matthew, Thorp, Stephen
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.12.2023
• Subjects: methods: statistical; cosmology: observations; dust, extinction; supernovae: general
• DOI: 10.1093/mnras/stad3159
• ISSN: 0035-8711

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.