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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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http://hal.in2p3.fr/in2p3-00790493
Contributor : Sylvie Flores <>
Submitted on : Wednesday, February 20, 2013 - 12:57:49 PM
Last modification on : Saturday, April 11, 2020 - 1:53:18 AM

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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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