Standardizing Type Ia Supernova Absolute Magnitudes Using Gaussian Process Data Regression

TL;DR

This paper introduces Gaussian process models for Type Ia supernova spectral energy distributions and absolute magnitudes, enabling improved calibration and potential cosmological parameter estimation from supernova data.

Contribution

It presents a novel Gaussian process-based framework for modeling supernova SEDs and magnitudes, directly informed by data, improving calibration precision.

Findings

Achieved 0.09-0.13 mag calibration accuracy in key bands.

Demonstrated the model on synthetic supernova data from the Nearby Supernova Factory.

Potential to standardize supernovae and fit cosmological parameters simultaneously.

Abstract

We present a novel class of models for Type Ia supernova time-evolving spectral energy distributions (SED) and absolute magnitudes: they are each modeled as stochastic functions described by Gaussian processes. The values of the SED and absolute magnitudes are defined through well-defined regression prescriptions, so that data directly inform the models. As a proof of concept, we implement a model for synthetic photometry built from the spectrophotometric time 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 mag in the $z=0.25$ blueshifted $i$-band, where the dispersion includes contributions from measurement uncertainties and peculiar velocities. The methodology can be applied to spectrophotometric time series of supernovae that span a range of redshifts to simultaneously standardize supernovae together with fitting cosmological parameters.