Precursor identification for strong flares based on anomaly detection algorithm - Wang, Jingjing; Luo, Bingxian; Liu, Siqing (Frontiers in Astronomy and Space Sciences, 2022)
In this study, we assume that the magnetic configuration of active regions (ARs) in quiet periods has certain similarity and can be considered as ``normal'' features. While, there are some other magnetic features of active regions that are related to strong flares. They can be considered as the precursor of strong flares and ``anomaly'' features. Our study is aiming to identify those ``anomaly'' and apply it in strong flares forecasting. An unsupervised auto-encoder network has been used to understand and memorize these ``normal'' features. And then based on the mean squared errors between the pictures of the ARs and the corresponding reconstructed pictures derived by the network, an anomaly detection algorithm has been adopted to identify the precursor for strong flares and develop a strong flare classification model. The strong-flare classification model reaches an F1 score of 0.8139, an accuracy of 0.8954, a recall of 0.8785, a precision of 0.7581. Moreover, for those correctly predicted strong-flare events (93 M- and above class flares), the model reaches an average first warning time of 45.24 hours. The results indicate that the anomaly detection algorithm can be used in precursor identification for strong flares and help in both improving strong-flare prediction accuracy and enlarging the time in advance. And the obtained average maximum warning period for strong-flare prediction (nearly two days) will be useful for future applications for space-weather solar-flare prediction.