zhy1109/detection-of-transient-phenomena-in-time-domain-astronomy

The code will be published after the paper is accepted

Detection of X-ray Bursts in Astronomical Time Series: the Burst of GRO J1744-28 as an Example

CNN-lstm is the network model code. It is suitable for detecting the change of time information in one-dimensional time series signal or picture. The '.m' file is the file of the model.

If the code is useful, we are welcome to cite the paper: 'Detection of X-ray Bursts in Astronomical Time Series: the Burst of GRO J1744-28 as an Example'

introduction: To automatically, accurately and fast detect local changes in time-series data continuously emitted by X-ray sources, an autoencoder-based unsupervised learning anomaly detection method is proposed. Here, we considered the X-ray burst of GRO J1744-28 as our case study. First, we tested the proposed method using simulation data and a test set based on a phenomenologically-motivated light curve fitting of different burst types. Our method exhibited superior performance, achieving F-scores of 0.969 and 0.936 for the detection of small bursts with low peak count rates such as structure bursts and microbursts, respectively. Then, based on Rossi X-ray Timing Detector (RXTE) observation data for GRO J1744-28 during the outburst period, we identified low-amplitude bursts using the proposed method and analyzed the burst regularity of GRO J1744-28. Our approach does not require complex modeling and has powerful feature extraction and detection capabilities, which can be used to automatically and efficiently detect changes in a data stream.