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Cited by 201Gamma-ray bursts and supernovaeGalaxies: Formation, Evolution, PhenomenaNeutrino Physics Research

The DOI matches its Crossref record; 74 of the 75 references it cites were found in a registry or an index.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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Lochner, Michelle, McEwen, Jason D., Peiris, Hiranya V., Lahav, Ofer, Winter, Max K.·2016·The Astrophysical Journal Supplement Series
DOI 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.

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Research works printed but not linked: 11

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Metadata

Authors
Lochner, M., McEwen, J. D., Peiris, H. V., Lahav, O., & Winter, M. K.
Affiliations
University College London (GB)
Corresponding author
Lochner, Michelle
ORCID
Venue
The Astrophysical Journal Supplement Series
Publisher
American Astronomical Society
Published
2016-08-01
Volume
225
Issue
2
Pages
31
ISSN
  • 0067-0049 (ISSN-L)
  • 1538-4365
DOI
10.3847/0067-0049/225/2/31
OpenAlex
W2292579160
Cited by
200
References
75
Citations by year
2026: 11 · 2025: 16 · 2024: 14 · 2023: 23 · 2022: 28 · 2021: 21 · 2020: 27 · 2019: 32 · 2018: 14 · 2017: 13 · 2016: 1
FWCI
9.72
Type
journal article
Language
en
Open access
Licence
Locations
  • The Astrophysical Journal Supplement Series (publishedVersion)
  • arXiv (Cornell University) (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Science and Technology Facilities Council (FP7/2007-2013, ST/N000811/1, ST/M00015X/1, ST/L00660X/1, ST/N00258X/1, ST/N002512/1)
  • FP7 Ideas: European Research Council (306478)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Gamma-ray bursts and supernovae
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Neutrino Physics Research
Keywords
supernova classification, photometric classification, wavelet feature extraction, boosted decision trees, supernova photometry, spectroscopic follow-up, multiband light curves

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