Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups

Yuanhui Fang,Yanmei Cui,Xianzhi Ao

doi:10.1155/2019/9196234

Abstract

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%.

Highlights

In 1908, Hale used the principle of the Zeeman effect to calculate the magnetic field strength inside sunspots and found that it is stronger than that of the surrounding area [1]
The paper is organized as follows: the Mount Wilson magnetic classification is briefly described in Section 2; both the data source and the preprocessing method are explained in Section 3; the structure of convolutional neural network (CNN) is illustrated in Section 4 together with the training results of models using different input data; Section 5 concludes the paper
In order to develop the automatic identification for the active regions (ARs) magnetic type based on the Mount Wilson classification scheme, we adopt CNN to train the SDO/HMI magnetogram and continuum image data from 2010 to 2017

Summary

Introduction

In 1908, Hale used the principle of the Zeeman effect to calculate the magnetic field strength inside sunspots and found that it is stronger than that of the surrounding area [1]. The increasing number of space missions has led to a rapid accumulation of solar activity data set It has been on the agenda for quite a while to implement automatic identification procedures for sunspot magnetic types. In this work, based on the Mount Wilson classification system, the magnetic types of ARs are labeled by one of the unipolar group Alpha, the bipolar group Beta, and other complex multipole groups, Beta-x. These three magnetic types are identified automatically by using the convolutional neural network (CNN) method with SDO/HMI data taken during the time interval 2010-2017. The paper is organized as follows: the Mount Wilson magnetic classification is briefly described in Section 2; both the data source and the preprocessing method are explained in Section 3; the structure of CNN is illustrated in Section 4 together with the training results of models using different input data; Section 5 concludes the paper

Mount Wilson Sunspot Classification Scheme

Data Preprocessing

Classification Model

Conclusion and Discussion