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. and Smith, M. (2011) Statistical classification techniques for photometric supernova typing. Monthly Notices of the Royal Astronomical Society, 414 3: 1987-2004. doi:10.1111/j.1365-2966.2011.18514.x

Author Newling, J.
Varughese, M.
Bassett, B.
Campbell, H.
Hlozek, R.
Kunz, M.
Lampeitl, H.
Martin, B.
Nichol, R.
Parkinson, D.
Smith, M.
Title Statistical classification techniques for photometric supernova typing
Journal name Monthly Notices of the Royal Astronomical Society   Check publisher's open access policy
ISSN 0035-8711
Publication date 2011-07
Sub-type Article (original research)
DOI 10.1111/j.1365-2966.2011.18514.x
Open Access Status DOI
Volume 414
Issue 3
Start page 1987
End page 2004
Total pages 18
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2012
Language eng
Abstract 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.
Keyword Methods: statistical
Techniques: photometric
Supernovae: general
Digital Sky Survey
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Accepted 2011 February 8. Received 2011 January 28; in original form 2010 October 20

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
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