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Application of Deep Learning to Solar and Space Weather Data

Published online by Cambridge University Press:  28 September 2023

Yong-Jae Moon Open the ORCID record for Yong-Jae Moon [Opens in a new window] ,
Harim Lee ,
Jihyeon Son ,
Suk-Kyung Sung ,
Kangwoo Yi ,
Hyun-Jin Jeong ,
Eunsu Park ,
Eun-Young Ji ,
Il-Hyun Cho  and
Bendict Lawrance
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Yong-Jae Moon
Affiliation:
School of Space Research, Kyung Hee University, Yongin, 17104, Republic of Korea Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Harim Lee
Affiliation:
Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Jihyeon Son
Affiliation:
School of Space Research, Kyung Hee University, Yongin, 17104, Republic of Korea
Suk-Kyung Sung
Affiliation:
Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Kangwoo Yi
Affiliation:
Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Hyun-Jin Jeong
Affiliation:
School of Space Research, Kyung Hee University, Yongin, 17104, Republic of Korea
Eunsu Park
Affiliation:
Korea Astronomy and Space Science Institute, Daejeon, 34055, Republic of Korea
Eun-Young Ji
Affiliation:
Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Il-Hyun Cho
Affiliation:
Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Bendict Lawrance
Affiliation:
Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University, Yongin, 17104, Republic of Korea
Daye Lim
Affiliation:
Centre for mathematical Plasma-Astrophysics, Department of Mathematics, KU Leuven Leuven, 3001, Belgium
Gyungin Shin
Affiliation:
Department of Engineering Science, University of Oxford, Oxford, United Kingdom
Sujin Lee
Affiliation:
Space Center, Republic of Korea Air Force, Gyeryong, Republic of Korea
Sumiaya Rahman
Affiliation:
School of Space Research, Kyung Hee University, Yongin, 17104, Republic of Korea
Taeyoung Kim
Affiliation:
School of Space Research, Kyung Hee University, Yongin, 17104, Republic of Korea AI Factory, Daejeon, Republic of Korea
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Abstract

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In this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.

Keywords

THE SUNSun: generalSun: magnetic fieldsmethods: data analysistechniques: image processing
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 18 , Symposium S372: The Era of Multi-Messenger Solar Physics , August 2022 , pp. 131 - 149
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

References

Bilitza, D., Altadill, D., Truhlik, V., et al. 2017, Space Weather, 15, 418 CrossRefGoogle Scholar
Bloomfield, D. S., Higgins, P. A., McAteer, R. T. J., & Gallagher, P. T. 2012, ApJ Letters, 747, L41 CrossRefGoogle Scholar
Colak, T. & Qahwaji, R. 2009, Space Weather, 7, S06001 CrossRefGoogle Scholar
de Wijn, A. G., Burkepile, J. T., Tomczyk, S., et al. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8444, Ground-based and Airborne Telescopes IV, ed. Stepp, L. M., Gilmozzi, R., & Hall, H. J., 84443NGoogle Scholar
Domingo, V., Fleck, B., & Poland, A. I. 1995, Solar Physics, 162, 1 CrossRefGoogle Scholar
Ermolli, I., Criscuoli, S., Centrone, M., Giorgi, F., & Penza, V. 2007, A&A, 465, 305 Google Scholar
Ermolli, I., Fofi, M., Bernacchia, C., et al. 1998, Solar Physics, 177, 1 10.1023/A:1004932431519CrossRefGoogle Scholar
Galilei, G., Welser, M., & de Filiis, A. 1613, Istoria E dimostrazioni intorno alle macchie solari E loro accidenti comprese in tre lettere scritte all’illvstrissimo signor Marco Velseri …Google Scholar
Gopalswamy, N., Yashiro, S., Michalek, G., et al. 2009, Earth Moon and Planets, 104, 295 CrossRefGoogle Scholar
Hochreiter, S. & Schmidhuber, J. 1997, Neural Comput., 9, 1735–1780 CrossRefGoogle Scholar
Huang, X., Wang, H., Xu, L., et al. 2018, ApJ, 856, 7 CrossRefGoogle Scholar
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. 2017, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Google Scholar
Jeong, H.-J., Moon, Y.-J., Park, E., & Lee, H. 2020, ApJ Letters, 903, L25 CrossRefGoogle Scholar
Jeong, H.-J., Moon, Y.-J., Park, E., Lee, H., & Baek, J.-H. 2022, The Astrophysical Journal Supplement Series, 262, 50 CrossRefGoogle Scholar
Ji, E.-Y., Moon, Y.-J., & Park, E. 2020, Space Weather, 18, e02411 CrossRefGoogle Scholar
Kaiser, M. L., Kucera, T. A., Davila, J. M., et al. 2008, Space Science Reviews, 136, 5 CrossRefGoogle Scholar
Keller, C. U., Harvey, J. W., & Giampapa, M. S. 2003, in Innovative telescopes and instrumentation for solar astrophysics, Vol. 4853, International Society for Optics and Photonics, 194–204 Google Scholar
Kim, T., Park, E., Lee, H., et al. 2019, Nature Astronomy, 3, 397 CrossRefGoogle Scholar
Kumar, S., Tan, E. L., & Murti, D. S. 2015, Earth, Planets and Space, 67, 42 CrossRefGoogle Scholar
Lawrance, B., Lee, H., Park, E., et al. 2022, ApJ, 937, 111 CrossRefGoogle Scholar
Lee, H., Park, E., & Moon, Y.-J. 2021a, ApJ, 907, 118 CrossRefGoogle Scholar
Lee, S., Ji, E.-Y., Moon, Y.-J., & Park, E. 2021b, Space Weather, 19, e2020SW002600CrossRefGoogle Scholar
Lemen, J. R., Title, A. M., Akin, D. J., et al. 2012, Solar Physics, 275, 17 CrossRefGoogle Scholar
Lim, D., Moon, Y.-J., Park, E., & Lee, J.-Y. 2021, ApJ Letters, 915, L31 CrossRefGoogle Scholar
Mirza, M. & Osindero, S. 2014, arXiv preprint arXiv:1411.1784Google Scholar
Park, E., Moon, Y.-J., Lim, D., & Lee, H. 2020, ApJ Letters, 891, L4 CrossRefGoogle Scholar
Park, E., Moon, Y.-J., Shin, S., et al. 2018, ApJ, 869, 91 CrossRefGoogle Scholar
Pesnell, W. D., Thompson, B. J., & Chamberlin, P. C. 2012, Solar Physics, 275, 3 CrossRefGoogle Scholar
Rahman, S., Moon, Y.-J., Park, E., et al. 2020, ApJ Letters, 897, L32 CrossRefGoogle Scholar
Schaer, S. 1999, Geod.-Geophys. Arb. Schweiz, 59CrossRefGoogle Scholar
Scherrer, P. H., Bogart, R. S., Bush, R. I., et al. 1995, Solar Physics, 162, 129 CrossRefGoogle Scholar
Schou, J., Scherrer, P. H., Bush, R. I., et al. 2012, Solar Physics, 275, 229 10.1007/s11207-011-9842-2CrossRefGoogle Scholar
Selvaraju, R. R., Cogswell, M., Das, A., et al. 2017, in 2017 IEEE International Conference on Computer Vision (ICCV), 618–626 Google Scholar
Shin, G., Moon, Y.-J., Park, E., et al. 2020, ApJ Letters, 895, L16 CrossRefGoogle Scholar
Son, J., Cha, J., Moon, Y.-J., et al. 2021, ApJ, 920, 101 CrossRefGoogle Scholar
Son, J., Moon, Y.-J., & Shin, S. 2022, Space Weather, 20, e2022SW003153CrossRefGoogle Scholar
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. 2014, Striving for Simplicity: The All Convolutional NetGoogle Scholar
Sutskever, I., Vinyals, O., & Le, Q. V. 2014, Sequence to Sequence Learning with Neural NetworksGoogle Scholar
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., et al. 2018, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8798–8807Google Scholar
Yi, K., Moon, Y.-J., Lim, D., Park, E., & Lee, H. 2021, ApJ, 910, 8 CrossRefGoogle Scholar
Yi, K., Moon, Y.-J., Shin, G., & Lim, D. 2020, ApJ Letters, 890, L5 CrossRefGoogle Scholar