首页|Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-Attention

Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-Attention

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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 waming 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.

methods:data analysisstars:variables:generaltechniques:photometric

Hao Li、Qing Zhao、Long Shao、Tao Liu、Chenzhou Cui、Yunfei Xu

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College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China

National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2022YFF0711500118030221227307712273077

2024

10.1088/1674-4527/ad3c6e

天文和天体物理学研究
中国科学院国家天文台

天文和天体物理学研究

CSTPCD
影响因子：0.406
ISSN：1674-4527
年,卷(期)：2024.24(5)
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