An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning
Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We h...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/ad2dea |
| _version_ | 1797214576049127424 |
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| author | Rongpei Lin Yi Yang Fang Shen Gilbert Pi Yucong Li |
| author_facet | Rongpei Lin Yi Yang Fang Shen Gilbert Pi Yucong Li |
| author_sort | Rongpei Lin |
| collection | DOAJ |
| description | Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME’s kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu’s method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME’s motion in time-series images and finally determine the CME’s kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs’ geoeffectiveness. |
| first_indexed | 2024-04-24T11:16:22Z |
| format | Article |
| id | doaj.art-b3d4d843d72d4f84a9a8fef671ec7ff7 |
| institution | Directory Open Access Journal |
| issn | 0067-0049 |
| language | English |
| last_indexed | 2024-04-24T11:16:22Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj.art-b3d4d843d72d4f84a9a8fef671ec7ff72024-04-11T08:28:30ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492024-01-0127125910.3847/1538-4365/ad2deaAn Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine LearningRongpei Lin0https://orcid.org/0009-0009-2573-5963Yi Yang1https://orcid.org/0000-0001-5355-5964Fang Shen2https://orcid.org/0000-0002-4935-6679Gilbert Pi3https://orcid.org/0000-0002-6039-3622Yucong Li4State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; yyang@swl.ac.cn, fshen@swl.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People's Republic of ChinaState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; yyang@swl.ac.cn, fshen@swl.ac.cnState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; yyang@swl.ac.cn, fshen@swl.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People's Republic of ChinaSpace Physics Group, Department of Surface and Plasma Science, Charles University , V Holesovickach 2, 180 00, Prague, Czech RepublicState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; yyang@swl.ac.cn, fshen@swl.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People's Republic of ChinaCoronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME’s kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu’s method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME’s motion in time-series images and finally determine the CME’s kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs’ geoeffectiveness.https://doi.org/10.3847/1538-4365/ad2deaSolar coronal mass ejectionsSpace weatherConvolutional neural networks |
| spellingShingle | Rongpei Lin Yi Yang Fang Shen Gilbert Pi Yucong Li An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning The Astrophysical Journal Supplement Series Solar coronal mass ejections Space weather Convolutional neural networks |
| title | An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning |
| title_full | An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning |
| title_fullStr | An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning |
| title_full_unstemmed | An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning |
| title_short | An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning |
| title_sort | algorithm for the determination of coronal mass ejection kinematic parameters based on machine learning |
| topic | Solar coronal mass ejections Space weather Convolutional neural networks |
| url | https://doi.org/10.3847/1538-4365/ad2dea |
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