Lochner, M;
McEwen, JD;
Peiris, HV;
Lahav, O;
Winter, MK;
(2016)
Photometric Supernova Classification With Machine Learning.
The Astrophysical Journal Supplement Series
, 225
(2)
10.3847/0067-0049/225/2/31.
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Abstract
Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.
| Type: | Article |
|---|---|
| Title: | Photometric Supernova Classification With Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3847/0067-0049/225/2/31 |
| Publisher version: | http://doi.org/10.3847/0067-0049/225/2/31 |
| Language: | English |
| Additional information: | © 2016. The American Astronomical Society. All rights reserved. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, Cosmology: Observations, Methods: Data Analysis, Supernovae: General, Dark Energy Survey, Neural-networks, Ia Supernovae, Sdss-ii, Component Analysis, Wavelets, Pan-starrs1, Regression, Cosmology, Invariant |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1508539 |
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