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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
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

Abstract

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.

Type
Contributed Paper
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

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