STANDARDIZING TYPE Ia SUPERNOVA ABSOLUTE MAGNITUDES USING GAUSSIAN PROCESS DATA REGRESSION

Abstract

We present a novel class of models for Type Ia supernova time-evolving\nspectral energy distributions (SED) and absolute magnitudes: they are each\nmodeled as stochastic functions described by Gaussian processes. The values of\nthe SED and absolute magnitudes are defined through well-defined regression\nprescriptions, so that data directly inform the models. As a proof of concept,\nwe implement a model for synthetic photometry built from the spectrophotometric\ntime series from the Nearby Supernova Factory. Absolute magnitudes at peak $B$\nbrightness are calibrated to 0.13 mag in the $g$-band and to as low as 0.09 mag\nin the $z=0.25$ blueshifted $i$-band, where the dispersion includes\ncontributions from measurement uncertainties and peculiar velocities. The\nmethodology can be applied to spectrophotometric time series of supernovae that\nspan a range of redshifts to simultaneously standardize supernovae together\nwith fitting cosmological parameters.\n