Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups
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 rapi...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Astronomy |
| Online Access: | http://dx.doi.org/10.1155/2019/9196234 |
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| author | Yuanhui Fang Yanmei Cui Xianzhi Ao |
| author_facet | Yuanhui Fang Yanmei Cui Xianzhi Ao |
| author_sort | Yuanhui Fang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-afab057adf354f30aa37db608b3dcda6 |
| institution | Kabale University |
| issn | 1687-7969 1687-7977 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Astronomy |
| spelling | doaj-art-afab057adf354f30aa37db608b3dcda62025-02-03T01:21:59ZengWileyAdvances in Astronomy1687-79691687-79772019-01-01201910.1155/2019/91962349196234Deep Learning for Automatic Recognition of Magnetic Type in Sunspot GroupsYuanhui Fang0Yanmei Cui1Xianzhi Ao2National Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaSunspots 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%.http://dx.doi.org/10.1155/2019/9196234 |
| spellingShingle | Yuanhui Fang Yanmei Cui Xianzhi Ao Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups Advances in Astronomy |
| title | Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups |
| title_full | Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups |
| title_fullStr | Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups |
| title_full_unstemmed | Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups |
| title_short | Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups |
| title_sort | deep learning for automatic recognition of magnetic type in sunspot groups |
| url | http://dx.doi.org/10.1155/2019/9196234 |
| work_keys_str_mv | AT yuanhuifang deeplearningforautomaticrecognitionofmagnetictypeinsunspotgroups AT yanmeicui deeplearningforautomaticrecognitionofmagnetictypeinsunspotgroups AT xianzhiao deeplearningforautomaticrecognitionofmagnetictypeinsunspotgroups |