The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model
Kaisey S. Mandel, Daniel Scolnic, Hikmatali Shariff, Ryan J. Foley and, Robert P. Kirshner

TL;DR
This paper develops a hierarchical Bayesian model to distinguish intrinsic supernova properties from host galaxy dust effects, improving the understanding of the color-magnitude relation and reducing systematic biases in cosmological measurements.
Contribution
It introduces a probabilistic generative model that separates intrinsic SN Ia variations from dust effects, providing more accurate estimates of the color-magnitude slope and dust law.
Findings
Intrinsic color-magnitude slope $eta_{int} = 2.3 \,\pm\, 0.3$
Dust law $R_B = 3.8 \,\pm\, 0.3$ consistent with Milky Way dust
Corrected systematic distance bias of approximately 0.10 mag
Abstract
Conventional Type Ia supernova (SN Ia) cosmology analyses currently use a simplistic linear regression of magnitude versus color and light curve shape, which does not model intrinsic SN Ia variations and host galaxy dust as physically distinct effects, resulting in low color-magnitude slopes. We construct a probabilistic generative model for the dusty distribution of extinguished absolute magnitudes and apparent colors as the convolution of a intrinsic SN Ia color-magnitude distribution and a host galaxy dust reddening-extinction distribution. If the intrinsic color-magnitude ( vs. ) slope differs from the host galaxy dust law , this convolution results in a specific curve of mean extinguished absolute magnitude vs. apparent color. The derivative of this curve smoothly transitions from in the blue tail to in the red tail of the apparent…
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