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

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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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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864
Analysis of a complex of statistical variables into principal components.
Hotelling, H. (1933) · Journal of Educational Psychology · doi:10.1037/h0071325 · Cited by 9,691

Show all 75 references

Research works printed but not linked: 11

Comon, 1994, SigPr, 36, 287
Cortes, 1995, Machine Learning, 20, 273
Fawcett, 2004, Machine Learning, 31, 1
Green, 1966, Society, 1, 521
Kunz, 2007, PhRvD, 75
Lo, 2009, 5:1
Pedregosa, 2011, Journal of Machine Learning Research, 12, 2825
Platt, 1999, Vol. 10, 61
Sammon, 1969, ITCmp, 18, 401
Van der Maaten, 2008, Journal of Machine Learning Research, 9, 85
Zhang, 2004, AA, 1, 3

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Authors: Lochner, M., McEwen, J. D., Peiris, H. V., Lahav, O., & Winter, M. K.
Affiliations: University College London (GB)
Corresponding author: Lochner, Michelle
ORCID: Lochner, Michelle
McEwen, Jason D.
Peiris, Hiranya V.
Lahav, Ofer
Winter, Max K.
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: bronze
doi.org
Licence: iopscience.iop.org/page/copyright (vor, 22 d)
iopscience.iop.org/info/page/text-and-data-mining (tdm, 22 d)
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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PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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