A hierarchical Bayesian SED model for Type Ia supernovae in the optical to near-infrared
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Abstract
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 (
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1365-2966
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European Commission Horizon 2020 (H2020) ERC (101002652)