2023MNRAS.526.5715W

To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, R_V, 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 R_V 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.