Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm
Solar energetic particles (SEPs) are a major source of space radiation, especially within the inner heliosphere. These particles, originating from solar flares and coronal mass ejections (CMEs), propagate primarily along interplanetary magnetic fields. The energy spectra of SEP events are crucial fo...
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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 Letters |
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| Online Access: | https://doi.org/10.3847/2041-8213/ad8bbc |
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| author | Jiajun Liu Zhendi Huang Jingnan Guo Yubao Wang Jiajia Liu |
| author_facet | Jiajun Liu Zhendi Huang Jingnan Guo Yubao Wang Jiajia Liu |
| author_sort | Jiajun Liu |
| collection | DOAJ |
| description | Solar energetic particles (SEPs) are a major source of space radiation, especially within the inner heliosphere. These particles, originating from solar flares and coronal mass ejections (CMEs), propagate primarily along interplanetary magnetic fields. The energy spectra of SEP events are crucial for assessing radiation effects and understanding the acceleration and propagation mechanisms in their source regions. In this study, we employed a decision tree regression algorithm with cost complexity pruning to predict SEP energy spectra, including peak flux and integral fluence spectra. This approach uses only solar flares, CMEs, and solar wind data as input parameters and demonstrates strong performance to accurately predict SEP spectra. This method holds significant real-time application value for monitoring and forecasting radiation risks in both deep space and near-Earth environments. |
| format | Article |
| id | doaj-art-caffe4332ceb4b30ab28041833b69f68 |
| institution | Kabale University |
| issn | 2041-8205 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj-art-caffe4332ceb4b30ab28041833b69f682024-11-08T13:32:15ZengIOP PublishingThe Astrophysical Journal Letters2041-82052024-01-019752L4310.3847/2041-8213/ad8bbcPredicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression AlgorithmJiajun Liu0https://orcid.org/0009-0001-5764-489XZhendi Huang1https://orcid.org/0009-0008-2221-0814Jingnan Guo2https://orcid.org/0000-0002-8707-076XYubao Wang3https://orcid.org/0009-0007-7848-1501Jiajia Liu4https://orcid.org/0000-0003-2569-1840Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China , Hefei 230026, People's Republic of China ; jnguo@ustc.edu.cnDeep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China , Hefei 230026, People's Republic of China ; jnguo@ustc.edu.cnDeep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China , Hefei 230026, People's Republic of China ; jnguo@ustc.edu.cn; Collaborative Innovation Center of Astronautical Science and Technology , Harbin 150001, People's Republic of ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China , Hefei 230026, People's Republic of China ; jnguo@ustc.edu.cnDeep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China , Hefei 230026, People's Republic of China ; jnguo@ustc.edu.cn; Collaborative Innovation Center of Astronautical Science and Technology , Harbin 150001, People's Republic of ChinaSolar energetic particles (SEPs) are a major source of space radiation, especially within the inner heliosphere. These particles, originating from solar flares and coronal mass ejections (CMEs), propagate primarily along interplanetary magnetic fields. The energy spectra of SEP events are crucial for assessing radiation effects and understanding the acceleration and propagation mechanisms in their source regions. In this study, we employed a decision tree regression algorithm with cost complexity pruning to predict SEP energy spectra, including peak flux and integral fluence spectra. This approach uses only solar flares, CMEs, and solar wind data as input parameters and demonstrates strong performance to accurately predict SEP spectra. This method holds significant real-time application value for monitoring and forecasting radiation risks in both deep space and near-Earth environments.https://doi.org/10.3847/2041-8213/ad8bbcSolar energetic particlesSpace weather |
| spellingShingle | Jiajun Liu Zhendi Huang Jingnan Guo Yubao Wang Jiajia Liu Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm The Astrophysical Journal Letters Solar energetic particles Space weather |
| title | Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm |
| title_full | Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm |
| title_fullStr | Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm |
| title_full_unstemmed | Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm |
| title_short | Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm |
| title_sort | predicting the energy spectra of solar energetic particles with a machine learning regression algorithm |
| topic | Solar energetic particles Space weather |
| url | https://doi.org/10.3847/2041-8213/ad8bbc |
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