摘要
Targeting the problem of high real-time requirements in astronomical data processing,this paper proposes a real-time early warning model for light curves based on a Gated Recurrent Unit(GRU)network.Using the memory function of the GRU network,a prediction model of the light curve is established,and the model is trained using the collected light curve data,so that the model can predict a star magnitude value for the next moment based on historical star magnitude data.In this paper,we calculate the difference between the model prediction value and the actual observation value and set a threshold.If the difference exceeds the set threshold,the observation value at the next moment is considered to be an abnormal value,and a warning is given.Astronomers can carry out further certification based on the early warning and in combination with other means of observation.The method proposed in this paper can be applied to real-time observations in time domain astronomy.
Targeting the problem of high real-time requirements in astronomical data processing, this paper proposes a real-time early warning model for light curves based on a Gated Recurrent Unit(GRU) network.Using the memory function of the GRU network, a prediction model of the light curve is established, and the model is trained using the collected light curve data, so that the model can predict a star magnitude value for the next moment based on historical star magnitude data. In this paper,we calculate the difference between the model prediction value and the actual observation value and set a threshold. If the difference exceeds the set threshold, the observation value at the next moment is considered to be an abnormal value,and a warning is given. Astronomers can carry out further certification based on the early warning and in combination with other means of observation. The method proposed in this paper can be applied to real-time observations in time domain astronomy.
基金
supported by the National Natural Science Foundation of China(No.41672323)
the Interdiscipline Research Funds of Beijing Normal University
