Real-time abnormal detection of GWAC light curve based on wavelettransform combined with GRU-Attention

Abstract 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 GWAC 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 DW(DiscreteWavelets)-GRU-Attention(GateRecurrentUnit-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 neural network, and 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 LSTM model, and an average improvement of 53.5% compared to the previous GRU model.Previous thesis work mainly detects whether the object is anomalous or not and fails to detect all the anomalous time nodes, the method proposed in this paper can cover most of the anomalous time nodes, and the addition of the Attention mechanism makes the key part of the light curves that determines the anomalies to be given a higher weight, and in the actual anomalies detection, the stars are 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.