Academic Journal
Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning
| العنوان: | Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning |
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| المؤلفون: | Hong JC(洪俊超), Liu H(刘辉), Bi Y(毕以), Xu, Zhe, Yang B(杨波), Yang JY(杨家艳), Su, Yang, Xia, Yuehan, Ji KF(季凯帆) |
| بيانات النشر: | IOP PUBLISHING LTD |
| سنة النشر: | 2021 |
| المجموعة: | Yunnan Observatories: YNAO OpenIR (Chinese Academy of Sciences, CAS) / 中国科学院云南天文台机构知识库 |
| مصطلحات موضوعية: | 天文学, 天文学::太阳与太阳系, 天文学::太阳与太阳系::太阳物理学, 计算机科学技术, 计算机科学技术::人工智能, 计算机科学技术::计算机应用, 理学, 理学::天文学, Astronomy & Astrophysics, ERUPTIONS, JETS |
| الوصف: | The 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. |
| نوع الوثيقة: | article in journal/newspaper report |
| اللغة: | English |
| Relation: | ASTROPHYSICAL JOURNAL; http://ir.ynao.ac.cn/handle/114a53/24453; https://dx.doi.org/10.3847/1538-4357/ac01d5 |
| DOI: | 10.3847/1538-4357/ac01d5 |
| الاتاحة: | http://ir.ynao.ac.cn/handle/114a53/24453 https://doi.org/10.3847/1538-4357/ac01d5 |
| Rights: | cn.org.cspace.api.content.CopyrightPolicy@78d702ba |
| رقم الانضمام: | edsbas.A8C8867F |
| قاعدة البيانات: | BASE |
| DOI: | 10.3847/1538-4357/ac01d5 |
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