Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning
We develop two artificial intelligence–based models (Models A and B) to predict time-evolving photospheric magnetic fields with an adjustable time step, ranging up to one solar rotation later. Model A predicts future magnetic field data using three consecutive radial magnetic field data sets, each 1...
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2025-01-01
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| Series: | The Astrophysical Journal Letters |
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| Online Access: | https://doi.org/10.3847/2041-8213/adf5c7 |
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| author | Hyun-Jin Jeong Stefaan Poedts Haopeng Wang Ekatarina Dineva Panagiotis Gonidakis Francesco Carella George Miloshevich Luis Linan Harim Lee |
| author_facet | Hyun-Jin Jeong Stefaan Poedts Haopeng Wang Ekatarina Dineva Panagiotis Gonidakis Francesco Carella George Miloshevich Luis Linan Harim Lee |
| author_sort | Hyun-Jin Jeong |
| collection | DOAJ |
| description | We develop two artificial intelligence–based models (Models A and B) to predict time-evolving photospheric magnetic fields with an adjustable time step, ranging up to one solar rotation later. Model A predicts future magnetic field data using three consecutive radial magnetic field data sets, each 12 hr apart. Model B reconstructs evolving magnetic fields using two sets of three consecutive data sets, one from a given date and the other from 27 days later, each 12 hr apart. To train and evaluate our models, we use a Pix2PixCC-based architecture and radial magnetic field data sets from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager during the solar maximum periods of 2012–2016 and 2021–2023. Models A and B successfully generate magnetic field data corresponding to the input prediction time step. Based on quantitative comparison, Model A outperforms the persistence model and performs comparably to the classical surface flux transport model. Model B shows improved performance compared to both models. We also compare predictions for National Oceanic and Atmospheric Administration Active Regions 12673 and 13664, which rapidly emerged and produced powerful solar flares. Notably, both Models A and B can predict increases in total unsigned magnetic fluxes several days in advance if their evolution is already captured in the input data. Model B also reconstructs radial magnetic fluxes that smoothly overlap between the input data and the model outputs. These results demonstrate the ability of our models to generate time-evolving magnetic field data flexibly and capture emerging magnetic fluxes, which are crucial for heliophysics research and space weather forecasting. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-01-01 |
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| series | The Astrophysical Journal Letters |
| spelling | doaj-art-9b23192030c944288d2ca507aa62ee552025-08-20T03:02:51ZengIOP PublishingThe Astrophysical Journal Letters2041-82052025-01-019892L3110.3847/2041-8213/adf5c7Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep LearningHyun-Jin Jeong0https://orcid.org/0000-0003-4616-947XStefaan Poedts1https://orcid.org/0000-0002-1743-0651Haopeng Wang2https://orcid.org/0000-0002-4217-6990Ekatarina Dineva3https://orcid.org/0000-0002-4645-4492Panagiotis Gonidakis4https://orcid.org/0000-0001-5797-0794Francesco Carella5https://orcid.org/0009-0009-3284-4340George Miloshevich6https://orcid.org/0000-0001-9896-1704Luis Linan7https://orcid.org/0000-0002-4014-1815Harim Lee8https://orcid.org/0000-0002-9300-8073Centre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.kr; School of Space Research, Kyung Hee University , Yongin, 17104, Republic of KoreaCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.kr; Institute of Physics, University of Maria Curie-Skłodowska , ul. Radziszewskiego 10, 20-031 Lublin, PolandCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.krCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.krCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.krCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.krCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.krCentre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven , Celestijnenlaan 200B, 3001 Leuven, Belgium ; hyun-jin.jeong@kuleuven.be, jeong_hj@khu.ac.krCenter for Solar-Terrestrial Research, New Jersey Institute of Technology , Newark, NJ 07102, USA; Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University , Yongin, 17104, Republic of KoreaWe develop two artificial intelligence–based models (Models A and B) to predict time-evolving photospheric magnetic fields with an adjustable time step, ranging up to one solar rotation later. Model A predicts future magnetic field data using three consecutive radial magnetic field data sets, each 12 hr apart. Model B reconstructs evolving magnetic fields using two sets of three consecutive data sets, one from a given date and the other from 27 days later, each 12 hr apart. To train and evaluate our models, we use a Pix2PixCC-based architecture and radial magnetic field data sets from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager during the solar maximum periods of 2012–2016 and 2021–2023. Models A and B successfully generate magnetic field data corresponding to the input prediction time step. Based on quantitative comparison, Model A outperforms the persistence model and performs comparably to the classical surface flux transport model. Model B shows improved performance compared to both models. We also compare predictions for National Oceanic and Atmospheric Administration Active Regions 12673 and 13664, which rapidly emerged and produced powerful solar flares. Notably, both Models A and B can predict increases in total unsigned magnetic fluxes several days in advance if their evolution is already captured in the input data. Model B also reconstructs radial magnetic fluxes that smoothly overlap between the input data and the model outputs. These results demonstrate the ability of our models to generate time-evolving magnetic field data flexibly and capture emerging magnetic fluxes, which are crucial for heliophysics research and space weather forecasting.https://doi.org/10.3847/2041-8213/adf5c7Solar magnetic fieldsThe SunAstronomy data analysisConvolutional neural networksSolar magnetic flux emergence |
| spellingShingle | Hyun-Jin Jeong Stefaan Poedts Haopeng Wang Ekatarina Dineva Panagiotis Gonidakis Francesco Carella George Miloshevich Luis Linan Harim Lee Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning The Astrophysical Journal Letters Solar magnetic fields The Sun Astronomy data analysis Convolutional neural networks Solar magnetic flux emergence |
| title | Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning |
| title_full | Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning |
| title_fullStr | Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning |
| title_full_unstemmed | Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning |
| title_short | Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning |
| title_sort | prediction of time evolving radial magnetic fields on the solar surface using deep learning |
| topic | Solar magnetic fields The Sun Astronomy data analysis Convolutional neural networks Solar magnetic flux emergence |
| url | https://doi.org/10.3847/2041-8213/adf5c7 |
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