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.
“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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“…these early efforts, there exists a consequent literature using pre deep learning machine learning methods (e.g. SVMs, RFs and ANNs) to address the problem of SN light curve classification (e.g. Lochner et al., 2016; Villar et al., 2019; Hosseinzadeh et al., 2020; Vargas dos Santos et al., 2020).”
BackgroundResultFully automated integral field spectrograph pipeline for the SEDMachine: pysedm
Rigault, M., Neill, J. D., Blagorodnova, N., Dugas, A., Feeney, M., Walters, R., Brinnel, V., Copin, Y., Fremling, C., Nordin, J., et al. (2019) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/201935344/pdf · Cited by 103 · see citing works
Unveiling phase transitions with machine learning
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Villar, V. A., Berger, E., Metzger, B. D., & Guillochon, J. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa8fcb · Cited by 92 · see citing works
Villar, V. A., Hosseinzadeh, G., Berger, E., Ntampaka, M., Jones, D. O., Challis, P., Chornock, R., Drout, M. R., Foley, R. J., Kirshner, R. P., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abc6fd · Cited by 92 · see citing works
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Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
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The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information
Traven, G., Matijevič, G., Zwitter, T., Žerjal, M., Kos, J., Asplund, M., Bland‐Hawthorn, J., Casey, A. R., Silva, G. M. D., Freeman, K. C., et al. (2017) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/228/2/24 · Cited by 75 · see citing works
Ishida, É. E. O., Beck, R. E., González-Gaitán, S., de Souza, R. S., Krone-Martins, A., Barrett, J. W., Kennamer, N., Vilalta, R., Burgess, J. M., Quint, B., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3015 · Cited by 74 · see citing works
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Machine learning cosmological structure formation
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Measuring the Growth Rate of Structure with Type IA Supernovae from LSST
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The miniJPAS survey: star-galaxy classification using machine learning
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DASH: Deep Learning for the Automated Spectral Classification of Supernovae and Their Hosts
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DeepSource : point source detection using deep learning
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Self-citationSCONE: Supernova Classification with a Convolutional Neural Network
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PELICAN: deeP architecturE for the LIght Curve ANalysis
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GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
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Scanning tunneling state recognition with multi-class neural network ensembles
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Anomaly Detection in the Open Supernova Catalog
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Assessment of supervised machine learning for atmospheric retrieval of exoplanets
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STACCATO: a novel solution to supernova photometric classification with biased training sets
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Cosmological parameter estimation from large-scale structure deep learning
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Measuring the Growth Rate of Structure with Type IA Supernovae from LSST
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A probabilistic approach to emission-line galaxy classification
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ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning
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“This is a major challenge for science applications such as photometric classification where labeled data sets tend to be heavily biased toward bright, low-redshift transients (Lochner et al. 2016; Boone 2019).”
BackgroundMethodMachine learning for observational cosmology
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Imbalance learning for variable star classification
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PELICAN: DeeP architecturE for the LIght Curve ANalysis
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The Impact of Observing Strategy on Cosmological Constraints with LSST
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Self-citationUnsupervised machine learning for transient discovery in deeper, wider, faster light curves
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Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
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Self-citationde Jaeger, T., Galbany, L., Filippenko, A. V., González-Gaitán, S., Yasuda, N., Maeda, K., Tanaka, M., Morokuma, T., Moriya, T. J., Tominaga, N., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2300 · Cited by 34 · see citing works
Predicting star formation properties of galaxies using deep learning
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The Discovery of a Gravitationally Lensed Supernova Ia at Redshift 2.22
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Fink: Early supernovae Ia classification using active learning
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A CNN adapted to time series for the classification of Supernovae
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Light-curve classification with recurrent neural networks for GOTO: dealing with imbalanced data
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Studying Type II supernovae as cosmological standard candles using the Dark Energy Survey
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ZTF SN Ia DR2: The diversity and relative rates of the thermonuclear supernova population
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Machine learning and the future of supernova cosmology
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Self-citationGagliano, A. T., Contardo, G., Foreman-Mackey, D. T., Malz, A. I., & Aleo, P. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ace326 · Cited by 27 · see citing works
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How to Find Variable Active Galactic Nuclei with Machine Learning
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Self-citationMachine-learning-based real–bogus system for the HSC-SSP moving object detection pipeline
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Photometric classification and redshift estimation of LSST Supernovae
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Cosmic Velocity Field Reconstruction Using AI
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Machine Learning for Asset Managers
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Inferring galaxy dark halo properties from visible matter with machine learning
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Baldeschi, A., Miller, A., Stroh, M., Margutti, R., & Coppejans, D. L. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abb1c0 · Cited by 18 · see citing works
A survey on machine learning based light curve analysis for variable astronomical sources
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On the amount of peculiar velocity field information in supernovae from LSST and beyond
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Photometric Light Curves Classification with Machine Learning
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deepSIP: linking Type Ia supernova spectra to photometric quantities with deep learning
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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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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