Papers › Bird-Snack: Bayesian Inference of dust law R_V Distributions using SN Ia Apparent...
Bird-Snack: Bayesian Inference of dust law R_V Distributions using SN Ia Apparent Colours at peaK
Sam M. Ward, Suhail Dhawan, Kaisey S. Mandel, Matthew Grayling, Stephen Thorp
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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, μ_(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 μ_(R_V)=2.61^(+0.38)_(-0.35), with 68%(95%) posterior upper bounds on the population dispersion, σ_(R_V)<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 datasets, for better interpretation of real-data inferences.
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