Standardizing Type Ia Supernova Absolute Magnitudes Using Gaussian Process Data Regression

A. G. Kim , R. C. Thomas , G. Aldering , P. Antilogus (1) , C. Aragon , S. Bailey , C. Baltay , S. Bongard (1) , C. Buton , A. Canto (1) , F. Cellier-Holzem (1) , M. Childress , N. Chotard , Y. Copin (2) , H. K. Fakhouri , E. Gangler (2) , J. Guy (1) , M. Kerschhaggl , M. Kowalski , J. Nordin , P. Nugent , K. Paech , R. Pain (1) , E. Pécontal (3) , R. Pereira (2) , S. Perlmutter , D. Rabinowitz , M. Rigault (2) , K. Runge , C. Saunders , R. Scalzo , G. Smadja (2) , C. Tao (4) , B. A. Weaver , C. Wu (1)

1 LPNHE - Laboratoire de Physique Nucléaire et de Hautes Énergies
2 IPNL - Institut de Physique Nucléaire de Lyon
3 CRAL - Centre de Recherche Astrophysique de Lyon
4 CPPM - Centre de Physique des Particules de Marseille

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.

Domaines: Cosmologie et astrophysique extra-galactique [astro-ph.CO]; Cosmologie et astrophysique extra-galactique [astro-ph.CO]

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, 2013, 766, pp.84. ⟨10.1088/0004-637X/766/2/84⟩. ⟨in2p3-00790493⟩