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We present a novel class of models for Type Ia supernova time-evolvingspectral energy distributions (SED) and absolute magnitudes: they are eachmodeled as stochastic functions described by Gaussian processes. The values ofthe SED and absolute magnitudes are defined through well-defined regressionprescriptions, so that data directly inform the models. As a proof of concept,we implement a model for synthetic photometry built from the spectrophotometrictime series from the Nearby Supernova Factory. Absolute magnitudes at peak $B$brightness are calibrated to 0.13 mag in the $g$-band and to as low as 0.09 magin the $z=0.25$ blueshifted $i$-band, where the dispersion includescontributions from measurement uncertainties and peculiar velocities. Themethodology can be applied to spectrophotometric time series of supernovae thatspan a range of redshifts to simultaneously standardize supernovae togetherwith fitting cosmological parameters.

Standardizing Type Ia Supernova Absolute Magnitudes Using Gaussian Process Data Regression