Automatic Coronagraph Image Classification with Machine Learning Methods

Abstract

The detection of Coronal Mass Ejections (CMEs) is an important prerequisite for establishing a CME event database and realizing the prediction of CME interplanetary propagation. The Lyman-alpha Solar Telescope (LST) aboard the ASO-S (Advanced Space-based Solar Observatory) satellite will be equipped with a white-light coronagraph. The images with CME detected will be contributed to various space weather prediction centers in China for CME early warning. The Visual Geometry Group (VGG) 16 convolutional neural network method is applied by us to automatically and effectively classify coronagraph images. Firstly, based on the image of the white light coronagraph of Large Angle and Spectrometric Coronagraph Experiment (LASCO) C2, we labeled the images according to whether a CME is observed. Then, the data set was used for training the VGG model. We find that the accuracy of the model in the test set classification reaches 92.5%. Next, according to the obtained classification results and combined with the space-time continuity rules, we corrected the mislabeling of images, and derived our final CME image sequences. Compared with the manual CME catalog of Coordinated Data Analysis Workshops (CDAW), the classified CME image sequences can include CME data more completely, and have higher detection sensitivity for weak CME structures.