基于深度学习的太阳黑子Wilson山磁类型识别方法

太阳黑子是太阳光球层中带有较强磁场的区域，通常是太阳爆发活动的源区。Wilson山磁分类是当前最为主流的太阳黑子分类方法之一，对研究太阳爆发有重要意义。利用2010－2017年间SDO/HMI成像仪观测到的720s_SHARP磁图和白光图数据，研究使用深度学习对太阳黑子群Wilson山磁分类的方法。实验结果表明，Xception网络在识别太阳黑子Wilson山磁类型上能取得最优的效果，其中对α类型黑子的F₁得分为96.50%，β类为93.20%，其他类型的黑子为84.65%。 

Recognition Method for Mount Wilson Magnetic Type of Sunspots Based on Deep Learning

Sunspots are the regions with stronger magnetic field in the solar photosphere and most of solar eruptions occur in complex sunspot groups. Mount Wilson magnetic classification is one of the most popular sunspots classification methods, which is of great significance to the study of solar eruptions. In recent years, with the rapid development of China’s space industry, space physics research has entered the era of big data. Deep learning methods for processing space science data are springing up. In this study, based on the SDO/HMI SHARP continuum and magnetogram data during 2010－2017, we propose to apply deep learning for the image recognition of Mount Wilson magnetic type of sunspots. The results show that Xception has a productive performance in the identification of the sunspots magnetic types in solar active regions. The F₁ score of sunspots group of α exceeds 96%, that of β is more than 93%, and that of other types is more than 84%.