# 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 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.
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Journal articles
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http://hal.in2p3.fr/in2p3-00790493
Contributor : Sylvie Flores Connect in order to contact the contributor
Submitted on : Wednesday, February 20, 2013 - 12:57:49 PM
Last modification on : Saturday, September 24, 2022 - 12:02:05 PM

### Citation

A. G. Kim, R. C. Thomas, G. Aldering, P. Antilogus, C. Aragon, et al.. Standardizing Type Ia Supernova Absolute Magnitudes Using Gaussian Process Data Regression. The Astrophysical Journal, American Astronomical Society, 2013, 766, pp.84. ⟨10.1088/0004-637X/766/2/84⟩. ⟨in2p3-00790493⟩

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