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Cited by 123Gamma-ray bursts and supernovaeGalaxies: Formation, Evolution, PhenomenaAstronomical Observations and Instrumentation

Deep Recurrent Neural Networks for Supernovae Classification

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Charnock, Tom, Moss, Adam·2017·The Astrophysical Journal Letters
DOI 10.3847/2041-8213/aa603d
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Abstract

We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50% of the representational SPCC data set (around 10 4 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F 1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1%, AUC of 0.977, and F 1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys.

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Crossref · 10.3847/2041-8213/aa603d

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  • Charnock & Moss (2017) report an average accuracy above 90% for the classification of light curves in three types-supernovae types I, II and III-on the simulated sample from the Supernovae Photometric Classification Challenge.

    “The authors report an average accuracy above 90% for the classification of light curves in three types-supernovae types I, II and III-on the simulated sample from the Supernovae Photometric Classification Challenge”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Charnock & Moss (2017)

  • One advantage of RNNs is the ability to classify incomplete light curves.

    “In addition of not requiring feature engineering, one advantage of RNNs is the ability to classify incomplete light curves.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Charnock & Moss (2017)

  • 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 Charnock & Moss (2017)

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Authors
Charnock, T., & Moss, A.
Affiliations
University of Nottingham (GB)
ORCID
Venue
The Astrophysical Journal Letters
Publisher
American Astronomical Society
Published
2017-03-10
Volume
837
Issue
2
Pages
L28
ISSN
  • 2041-8205 (ISSN-L)
  • 2041-8213
DOI
10.3847/2041-8213/aa603d
OpenAlex
W2464878756
Cited by
123
References
7
Citations by year
2026: 8 · 2025: 10 · 2024: 6 · 2023: 14 · 2022: 16 · 2021: 16 · 2020: 19 · 2019: 23 · 2018: 6 · 2017: 5
FWCI
8.08
Type
journal article
Language
en
Open access
Licence
Locations
  • The Astrophysical Journal Letters (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
Science and Technology Facilities Council (ST/L000393/1)
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 › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
Keywords
deep recurrent neural networks, light curves, Type Ia supernovae, bidirectional neural network, photometric surveys

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