A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical
  to Near-Infrared
  While conventional Type Ia supernova (SN Ia) cosmology analyses rely
primarily on rest-frame optical light curves to determine distances, SNe Ia are
excellent standard candles in near-infrared (NIR) light, which is significantly
less sensitive to dust extinction. A SN Ia spectral energy distribution (SED)
model capable of fitting rest-frame NIR observations is necessary to fully
leverage current and future SN Ia datasets from ground- and space-based
telescopes including HST, LSST, JWST, and RST. We construct a hierarchical
Bayesian model for SN Ia SEDs, continuous over time and wavelength, from the
optical to NIR ($B$ through $H$, or $0.35 -1.8\, \mu$m). We model the SED as a
combination of physically-distinct host galaxy dust and intrinsic spectral
components. The distribution of intrinsic SEDs over time and wavelength is
modelled with probabilistic functional principal components and the covariance
of residual functions. We train the model on a nearby sample of 79 SNe Ia with
joint optical and NIR light curves by sampling the global posterior
distribution over dust and intrinsic latent variables, SED components, and
population hyperparameters. The photometric distances of SNe Ia with NIR data
near maximum light obtain a total RMS error of 0.10 mag with our BayeSN model,
compared to 0.14 mag with SNooPy and SALT2 for the same sample. Jointly fitting
the optical and NIR data of the full sample for a global host dust law, we find
$R_V = 2.9 \pm 0.2$, consistent with the Milky Way average.
