Research on Mount Wilson Magnetic Classification Based on Deep Learning

Abstract

The Mount Wilson magnetic classification of sunspot groups is thought to be meaningful to forecast flares' eruptions. In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. It includes three stages, generating object locations, detecting objects, and merging detections. The key technologies consist of the backbone as Hourglass-54, the attention mechanism, and the key points' mechanism including the top-left corners and the bottom-right corners of the object by corner pooling layers. These technologies improve the efficiency of detecting the objects without sacrificing accuracy. A dataset is built by a total of 2486 composited images which are composited with the continuum images and the corresponding magnetograms from HMI and MDI. After training the network, the sunspot groups in a composited solar full image are detected and classified in 3 seconds on average. The test results show that this method has a good performance, with the accuracy, precision, recall, and mAP as 0.94, 0.93, 0.94, and 0.90, respectively. Moreover, the flare productivities of different types of sunspot groups from 2011 to 2020 are calculated. As Itot ≥ 1, the flare productivities of α,β,βγ,βδ, and βγδ sunspot groups are 0.14, 0.28, 0.61, 0.71, and 0.87, respectively. As Itot ≥ 10, the flare productivities are 0.02, 0.07, 0.27, 0.45, and 0.65, respectively. It means that the βγ,βδ, and βγδ types are indeed very closely related to the eruption of solar flares, especially the βγδ type. Based on the reliability of this method, the sunspot groups of the HMI solar full images from 2011 to 2020 are detected and classified, and the detailed data are shared on the website (https://61.166.157.71/MWMCSG.html).