Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-Attention Article Swipe
Hao Li
,
Qing Zhao
,
Long Shao
,
Tao Liu
,
Chenzhou Cui
,
Yunfei Xu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1674-4527/ad3c6e
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1674-4527/ad3c6e
Nowadays, astronomy has entered the era of Time-Domain Astronomy, and the study of the time-varying light curves of various types of objects is of great significance in revealing the physical properties and evolutionary history of celestial bodies. The Ground-based Wide Angle Cameras telescope, on which this paper is based, has observed more than 10 million light curves, and the detection of anomalies in the light curves can be used to rapidly detect transient rare phenomena such as microgravity lensing events from the massive data. However, the traditional statistically based anomaly detection methods cannot realize the fast processing of massive data. In this paper, we propose a Discrete Wavelet (DW)-Gate Recurrent Unit-Attention (GRU-Attention) light curve warning model. Wavelet transform has good effect on data noise reduction processing and feature extraction, which can provide richer and more stable input features for a neural network, and the neural network can provide more flexible and powerful output model for wavelet transform. Comparison experiments show an average improvement of 61% compared to the previous pure long-short-term memory unit (LSTM) model, and an average improvement of 53.5% compared to the previous GRU model. The efficiency and accuracy of anomaly detection in previous paper work are not good enough, the method proposed in this paper possesses higher efficiency and accuracy, which incorporates the Attention mechanism to find out the key parts of the light curve that determine the anomalies. These parts are assigned higher weights, and in the actual anomaly detection, the star is detected with 83.35% anomalies on average, and the DW-GRU-Attention model is compared with the DW-LSTM model, and the detection result f1 is improved by 5.75% on average, while having less training time, thus providing valuable information and guidance for astronomical observation and research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1674-4527/ad3c6e
- https://iopscience.iop.org/article/10.1088/1674-4527/ad3c6e/pdf
- OA Status
- hybrid
- Cited By
- 2
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394623458
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394623458Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1674-4527/ad3c6eDigital Object Identifier
- Title
-
Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-AttentionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-09Full publication date if available
- Authors
-
Hao Li, Qing Zhao, Long Shao, Tao Liu, Chenzhou Cui, Yunfei XuList of authors in order
- Landing page
-
https://doi.org/10.1088/1674-4527/ad3c6ePublisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1674-4527/ad3c6e/pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1674-4527/ad3c6e/pdfDirect OA link when available
- Concepts
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Light curve, Wavelet, Wavelet transform, Physics, Artificial intelligence, Anomaly detection, Artificial neural network, Pattern recognition (psychology), Noise reduction, Discrete wavelet transform, Algorithm, Astrophysics, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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16Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-04-09 |
| publication_year | 2024 |
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