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
Open accessAbstract
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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“Despite the small training set of a hundred data points, RNNs achieved state-of-the-art results compared with a combination of template fits and boosted decision trees (Lochner et al. 2016).”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Lochner et al. 2016
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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
- 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
- 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
- bronze
- doi.org
- 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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