Sunspots are darker areas on the Sun’s photosphere and most of solar eruptions occur in complex sunspot groups. The Mount Wilson classification scheme describes the spatial distribution of magnetic polarities in sunspot groups, which plays an important role in forecasting solar flares. With the rapid accumulation of solar observation data, automatic recognition of magnetic type in sunspot groups is imperative for prompt solar eruption forecast. We present in this study, based on the SDO/HMI SHARP data taken during the time interval 2010-2017, an automatic procedure for the recognition of the predefined magnetic types in sunspot groups utilizing a convolutional neural network (CNN) method. Three different models (A, B, and C) take magnetograms, continuum images, and the two-channel pictures as input, respectively. The results show that CNN has a productive performance in identification of the magnetic types in solar active regions (ARs). The best recognition result emerges when continuum images are used as input data solely, and the total accuracy exceeds 95%, for which the recognition accuracy of Alpha type reaches 98% while the accuracy for Beta type is slightly lower but maintains above 88%.

中文翻译：

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深度学习用于黑子群中磁性类型的自动识别

太阳黑子是太阳光球上较暗的区域，大部分太阳爆发都发生在复杂的太阳黑子群中。威尔逊山分类方案描述了黑子群中磁极的空间分布，这在预测太阳耀斑中起着重要作用。随着太阳观测数据的迅速积累，太阳黑子群中磁性类型的自动识别对于迅速的太阳爆发预报至关重要。我们在本研究中基于2010-2017年时间间隔内获取的SDO / HMI SHARP数据，提出了一种利用卷积神经网络（CNN）方法识别黑子组中预定义磁性类型的自动程序。三种不同的模型（A，B和C）分别将磁描记图，连续图像和两通道图片作为输入。结果表明，CNN在识别太阳活动区域（AR）中的磁性类型方面具有生产性。当仅将连续图像用作输入数据时，最佳识别结果出现，并且总准确度超过95％，其中Alpha类型的识别准确度达到98％，而Beta类型的识别准确度稍低，但保持在88％以上。