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Improved KPCA for supernova photometric classification

Published online by Cambridge University Press:  01 July 2015

Emille E. O. Ishida
Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, D-85748 Garching, Germany email:
Filipe B. Abdalla
Department of Physics and Astronomy, University College London, London WC1E 6BT, UK email:
Rafael S. de Souza
Korea Astronomy & Space Science Institute, Daejeon 305-348, Korea MTA Eötvös University, EIRSA “Lendulet” Astrophysics Research Group, Budapest 1117, Hungary email:
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The problem of supernova photometric identification is still an open issue faced by large photometric surveys. In a previous investigation, we showed how combining Kernel Principal Component Analysis and Nearest Neighbour algorithms enable us to photometrically classify supernovae with a high rate of success. In the present work, we demonstrate that the introduction of Gaussian Process Regression (GPR) in determining each light curve highly improves the efficiency and purity rates. We present detailed comparison with results from the literature, based on the same simulated data set. The method proved to be satisfactorily efficient, providing high purity (⩽ 96%) rates when compared with standard algorithms, without demanding any information on astrophysical properties of the local environment, host galaxy or redshift.

Contributed Papers
Copyright © International Astronomical Union 2015 


Ishida, E. E. O. & de Souza, R. S. 2013, MNRAS, 430, 509CrossRefGoogle Scholar
Jolliffe, I. T., 2002, Principal Component Analysis, Springer-Verlag New York, Inc.Google Scholar
Karpenka, N. V., Feroz, F., & Hobson, M. P. 2013, MNRAS, 429, 1278CrossRefGoogle Scholar
Kessler, R., et al. 2010, PASP, 122, 1415CrossRefGoogle Scholar
Rasmussen, C. E., & Williams, C. K. I. 2006, GP for Machine Learning, MIT PressGoogle Scholar
Richards, J. al. 2012, MNRAS, 419, 1121CrossRefGoogle Scholar
Sako, al. 2014, arXiv:astro-ph/1401.3317Google Scholar