Acta Optica Sinica, Volume. 44, Issue 24, 2430005(2024)

An Early Classification Algorithm for Small Sample Transient Source Based on Machine Learning

Mengci Li1,2, Chengzhi Liu1,2,3、*, Chao Wu2,4、**, Zhe Kang1,2, Shiyu Deng1,4, and Zhenwei Li1,2
Author Affiliations
  • 1Changchun Observatory, National Astronomical Observatories, Chinese Academy of Sciences, Changchun 130117, Jilin , China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Key Laboratory of Space Object & Debris Observation, Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210008, Jiangsu , China
  • 4National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
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    References(21)

    [1] Ivezić Ž, Kahn S M, Tyson J A et al. LSST: from science drivers to reference design and anticipated data products[J]. The Astrophysical Journal, 873, 111(2019).

    [4] Abbott B P, Abbott R, Abbott T D et al. Observation of gravitational waves from a binary black hole merger[J]. Physical Review Letters, 116, 061102(2016).

    [15] Deng S Y, Liu C Z, Tan Y et al. A combination of multiple deep learning methods applied to small-sample space objects classification[J]. Spectroscopy and Spectral Analysis, 42, 609-615(2022).

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    Mengci Li, Chengzhi Liu, Chao Wu, Zhe Kang, Shiyu Deng, Zhenwei Li. An Early Classification Algorithm for Small Sample Transient Source Based on Machine Learning[J]. Acta Optica Sinica, 2024, 44(24): 2430005

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    Paper Information

    Category: Spectroscopy

    Received: May. 21, 2024

    Accepted: Jun. 24, 2024

    Published Online: Dec. 19, 2024

    The Author Email: Liu Chengzhi (lcz@cho.ac.cn), Wu Chao (cwu@nao.cas.cn)

    DOI:10.3788/AOS241045

    CSTR:32393.14.AOS241045

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