Single-Epoch Supernova Classification with Deep Convolutional Neural Networks

Akisato Kimura, Ichiro Takahashi, Masaomi Tanaka, Naoki Yasuda, Naonori Ueda, Naoki Yoshida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. The current photometric supernova surveys produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminances and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.

Original languageEnglish
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
EditorsJoao E. Ferreira, Teruo Higashino, Aibek Musaev
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages354-359
Number of pages6
ISBN (Electronic)9781538632925
DOIs
Publication statusPublished - 2017 Jul 13
Externally publishedYes
Event37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017 - Atlanta, United States
Duration: 2017 Jun 52017 Jun 8

Publication series

NameProceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017

Other

Other37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
CountryUnited States
CityAtlanta
Period17/6/517/6/8

Keywords

  • Supernova
  • convolutional neural network
  • deep learning
  • image classification
  • redshift

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems

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  • Cite this

    Kimura, A., Takahashi, I., Tanaka, M., Yasuda, N., Ueda, N., & Yoshida, N. (2017). Single-Epoch Supernova Classification with Deep Convolutional Neural Networks. In J. E. Ferreira, T. Higashino, & A. Musaev (Eds.), Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017 (pp. 354-359). [7979846] (Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCSW.2017.47