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Statistical classification techniques for photometric supernova typing

Newling, J. ; Varughese, M. ; Bassett, B. ; Campbell, H. ; Hlozek, R. ; Kunz, M. ; Lampeitl, H. ; Martin, B. ; Nichol, R. ; Parkinson, D. ; Smith, M.

In: Monthly Notices of the Royal Astronomical Society, 2011, vol. 414, no. 3, p. 1987-2004

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    Titre
    • Statistical classification techniques for photometric supernova typing
    Auteur
    • Newling, J.. Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701, South Africa
    • Varughese, M.. Department of Statistical Sciences, University of Cape Town, Rondebosch 7701, South Africa
    • Bassett, B.. Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701, South Africa
    • Campbell, H.. Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX
    • Hlozek, R.. Department of Astrophysics, Oxford University, Oxford OX1 3RH
    • Kunz, M.. Département de Physique Théorique, Université de Genève, Genève CH1211, Switzerland
    • Lampeitl, H.. Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX
    • Martin, B.. African Institute for Mathematical Sciences, 6-8 Melrose Road, Muizenberg 7945, South Africa
    • Nichol, R.. Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX
    • Parkinson, D.. Astronomy Centre, University of Sussex, Brighton BN1 9QH
    • Smith, M.. Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701, South Africa
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Monthly Notices of the Royal Astronomical Society, 2011, vol. 414, no. 3, p. 1987-2004. Blackwell Publishing Ltd
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:294976
    Summary
    Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20 000 simulated Dark Energy Survey light curves. We demonstrate that these methods perform very well provided a representative sample of the full population is used for training. Interestingly, we find that they do not require the redshift of the host galaxy or candidate supernova. However, training on the types of spectroscopic subsamples currently produced by supernova surveys leads to poor performance due to the resulting bias in training, and we recommend that special attention be given to the creation of representative training samples. We show that given a typical non-representative training sample, S, one can expect to pull out a representative subsample of about 10 per cent of the size of S, which is large enough to outperform the methods trained on all of S