Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model
In this study, we develop an artificial intelligence (AI)-based solar surface flux transport (SFT) model. We predict synoptic maps for the next solar rotation (27.2753 days) using deep learning. Our model takes the latest synoptic maps and their sine-latitude grid data as inputs. Synoptic maps, whic...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/adc447 |
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| author | Hyun-Jin Jeong Mingyu Jeon Daeil Kim Youngjae Kim Ji-Hye Baek Yong-Jae Moon Seonghwan Choi |
| author_facet | Hyun-Jin Jeong Mingyu Jeon Daeil Kim Youngjae Kim Ji-Hye Baek Yong-Jae Moon Seonghwan Choi |
| author_sort | Hyun-Jin Jeong |
| collection | DOAJ |
| description | In this study, we develop an artificial intelligence (AI)-based solar surface flux transport (SFT) model. We predict synoptic maps for the next solar rotation (27.2753 days) using deep learning. Our model takes the latest synoptic maps and their sine-latitude grid data as inputs. Synoptic maps, which represent global magnetic field distributions on the solar surface, have been widely used as initial boundary conditions in the Sun and space-weather prediction models. Here we train and evaluate our deep-learning model, based on the Pix2PixCC architecture, using data sets of Solar Dynamics Observatory/Helioseismic and Magnetic Imager, Solar and Heliospheric Observatory/Michelson Doppler Imager, and National Solar Observatory/Global Oscillation Network Group synoptic maps with a resolution of 360 by 180 (longitude and sine latitude) from 1996 to 2023. We present results of our model and compare them with those from the persistent model and the conventional SFT model, including the effects of differential rotation, meridional flow, and diffusion on the solar surface. The average pixel-to-pixel correlation coefficient between the target and our AI-generated data, after 10 by 10 binning with a 10° resolution in longitude, is 0.71. This result is qualitatively similar to the results of the conventional SFT model (0.65–0.68) and better than the results of the persistent model (0.56). Our model successfully generates magnetic features, such as the diffusion of solar active regions and the motions of supergranules. Using synthetic input data with bipolar structures, we confirm that our model successfully reproduces differential rotation and meridional flow. Finally, we discuss the advantages and limitations of our model in view of magnetic field evolution and its potential applications. |
| format | Article |
| id | doaj-art-433a91a9becc4a35a508d2f07cb5831a |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-433a91a9becc4a35a508d2f07cb5831a2025-08-20T03:45:38ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-012781510.3847/1538-4365/adc447Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport ModelHyun-Jin Jeong0https://orcid.org/0000-0003-4616-947XMingyu Jeon1https://orcid.org/0009-0004-7798-5052Daeil Kim2https://orcid.org/0009-0008-5566-6084Youngjae Kim3https://orcid.org/0009-0009-2316-3658Ji-Hye Baek4https://orcid.org/0000-0002-0230-4417Yong-Jae Moon5https://orcid.org/0000-0001-6216-6944Seonghwan Choi6https://orcid.org/0000-0002-1946-7327Centre for mathematical Plasma Astrophysics , Department of Mathematics, KU Leuven, Celestijnenlaan 200B, 3001 Leuven, Belgium ; jeong_hj@khu.ac.kr; School of Space Research, Kyung Hee University , Yongin, 17104, Republic of Korea ; moonyj@khu.ac.krSchool of Space Research, Kyung Hee University , Yongin, 17104, Republic of Korea ; moonyj@khu.ac.krSchool of Space Research, Kyung Hee University , Yongin, 17104, Republic of Korea ; moonyj@khu.ac.krSchool of Space Research, Kyung Hee University , Yongin, 17104, Republic of Korea ; moonyj@khu.ac.krTechnology Center for Astronomy and Space Science, Korea Astronomy and Space Science Institute , Daejeon, 34055, Republic of Korea; Space Science Division, Korea Astronomy and Space Science Institute , Korea Astronomy and Space Science Institute, Daejeon, 34055, Republic of KoreaSchool of Space Research, Kyung Hee University , Yongin, 17104, Republic of Korea ; moonyj@khu.ac.kr; Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University , Yongin, 17104, Republic of KoreaTechnology Center for Astronomy and Space Science, Korea Astronomy and Space Science Institute , Daejeon, 34055, Republic of Korea; Space Science Division, Korea Astronomy and Space Science Institute , Korea Astronomy and Space Science Institute, Daejeon, 34055, Republic of KoreaIn this study, we develop an artificial intelligence (AI)-based solar surface flux transport (SFT) model. We predict synoptic maps for the next solar rotation (27.2753 days) using deep learning. Our model takes the latest synoptic maps and their sine-latitude grid data as inputs. Synoptic maps, which represent global magnetic field distributions on the solar surface, have been widely used as initial boundary conditions in the Sun and space-weather prediction models. Here we train and evaluate our deep-learning model, based on the Pix2PixCC architecture, using data sets of Solar Dynamics Observatory/Helioseismic and Magnetic Imager, Solar and Heliospheric Observatory/Michelson Doppler Imager, and National Solar Observatory/Global Oscillation Network Group synoptic maps with a resolution of 360 by 180 (longitude and sine latitude) from 1996 to 2023. We present results of our model and compare them with those from the persistent model and the conventional SFT model, including the effects of differential rotation, meridional flow, and diffusion on the solar surface. The average pixel-to-pixel correlation coefficient between the target and our AI-generated data, after 10 by 10 binning with a 10° resolution in longitude, is 0.71. This result is qualitatively similar to the results of the conventional SFT model (0.65–0.68) and better than the results of the persistent model (0.56). Our model successfully generates magnetic features, such as the diffusion of solar active regions and the motions of supergranules. Using synthetic input data with bipolar structures, we confirm that our model successfully reproduces differential rotation and meridional flow. Finally, we discuss the advantages and limitations of our model in view of magnetic field evolution and its potential applications.https://doi.org/10.3847/1538-4365/adc447Solar magnetic fieldsThe SunAstronomy data analysisConvolutional neural networks |
| spellingShingle | Hyun-Jin Jeong Mingyu Jeon Daeil Kim Youngjae Kim Ji-Hye Baek Yong-Jae Moon Seonghwan Choi Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model The Astrophysical Journal Supplement Series Solar magnetic fields The Sun Astronomy data analysis Convolutional neural networks |
| title | Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model |
| title_full | Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model |
| title_fullStr | Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model |
| title_full_unstemmed | Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model |
| title_short | Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model |
| title_sort | prediction of the next solar rotation synoptic maps using an artificial intelligence based surface flux transport model |
| topic | Solar magnetic fields The Sun Astronomy data analysis Convolutional neural networks |
| url | https://doi.org/10.3847/1538-4365/adc447 |
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