The Type Ia Supernova Color–Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

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 an intrinsic SN Ia color-magnitude distribution and a host galaxy dust reddening-extinction distribution. If the intrinsic color-magnitude (M-B versus B-V) slope beta(int) differs from the host galaxy dust law R-B, this convolution results in a specific curve of mean extinguished absolute magnitude versus apparent color. The derivative of this curve smoothly transitions from beta(int) in the blue tail to R-B in the red tail of the apparent color distribution. The conventional linear fit approximates this effective curve near the average apparent color, resulting in an apparent slope beta(app) between beta(int) and R-B. We incorporate these effects into a hierarchical Bayesian statistical model for SN Ia light curve measurements, and analyze a data set of SALT2 optical light curve fits of 248 nearby SNe Ia at z < 0.10. The conventional linear fit gives beta(app) approximate to 3. Our model finds beta(int) = 2.3 +/- 0.3 and a distinct dust law of R-B = 3.8 +/- 0.3, consistent with the average for Milky Way dust, while correcting a systematic distance bias of similar to 0.10 mag in the tails of the apparent color distribution. Finally, we extend our model to examine the SN Ia luminosity-host mass dependence in terms of intrinsic and dust components.