NAOC Open IR
Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning
Hong, Junchao1,2,3; Liu, Hui1,2; Bi, Yi1,2; Xu, Zhe2,4; Yang, Bo1,2; Yang, Jiayan1,2; Su, Yang5,6; Xia, Yuehan5,6; Ji, Kaifan1,2
2021-07-01
Source PublicationASTROPHYSICAL JOURNAL
ISSN0004-637X
Volume915Issue:2Pages:12
AbstractThe full-Sun corona is now imaged every 12 s in extreme ultraviolet (EUV) passbands by Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA), whereas it is only observed several times a day at X-ray wavelengths by Hinode/X-Ray Telescope (XRT). In this paper, we apply a deep-learning method, i.e., the convolution neural network (CNN), to establish data-driven models to generate full-Sun X-ray images in XRT filters from AIA EUV images. The CNN models are trained using a number of data pairs of AIA six-passband (171, 193, 211, 335, 131, and 94 angstrom) images and the corresponding XRT images in three filters: "Al_mesh," "Ti_poly," and "Be_thin." It is found that the CNN models predict X-ray images in good consistency with the corresponding well-observed XRT data. In addition, the purely data-driven CNN models are better than the conventional analysis method of the coronal differential emission measure (DEM) in predicting XRT-like observations from AIA data. Therefore, under conditions where AIA provides coronal EUV data well, the CNN models can be applied to fill the gap in limited full-Sun coronal X-ray observations and improve pool-observed XRT data. It is also found that DEM inversions using AIA data and our deep-learning-predicted X-ray data jointly are better than those using AIA data alone. This work indicates that deep-learning methods provide the opportunity to study the Sun based on virtual solar observation in future.
Funding OrganizationNational Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program ; National Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program ; National Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program ; National Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program
DOI10.3847/1538-4357/ac01d5
WOS KeywordERUPTIONS ; JETS
Language英语
Funding ProjectNational Key R&D Program of China[2019YFA0405000] ; Natural Science Foundation of China[11633008] ; Natural Science Foundation of China[U2031140] ; Natural Science Foundation of China[11873088] ; Natural Science Foundation of China[12073072] ; Natural Science Foundation of China[11933009] ; Natural Science Foundation of China[11873027] ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences[KLSA202005] ; CAS program[QYZDJ-SSW-SLH012]
Funding OrganizationNational Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program ; National Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program ; National Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program ; National Key R&D Program of China ; National Key R&D Program of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; CAS "Light of West China" Program ; CAS "Light of West China" Program ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of Chinese Academy of Sciences ; CAS program ; CAS program
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000672939200001
PublisherIOP PUBLISHING LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/76973
Collection中国科学院国家天文台
Corresponding AuthorHong, Junchao
Affiliation1.Chinese Acad Sci, Yunnan Observ, Kunming 650216, Yunnan, Peoples R China
2.Chinese Acad Sci, Ctr Astron Megasci, Beijing 100012, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China
4.Chinese Acad Sci, Purple Mt Observ, Nanjing 210034, Peoples R China
5.Chinese Acad Sci, Purple Mt Observ, Key Lab Dark Matter & Space Astron, Nanjing 210023, Peoples R China
6.Univ Sci & Technol China, Sch Astron & Space Sci, Hefei 230026, Peoples R China
Recommended Citation
GB/T 7714
Hong, Junchao,Liu, Hui,Bi, Yi,et al. Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning[J]. ASTROPHYSICAL JOURNAL,2021,915(2):12.
APA Hong, Junchao.,Liu, Hui.,Bi, Yi.,Xu, Zhe.,Yang, Bo.,...&Ji, Kaifan.(2021).Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning.ASTROPHYSICAL JOURNAL,915(2),12.
MLA Hong, Junchao,et al."Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning".ASTROPHYSICAL JOURNAL 915.2(2021):12.
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