A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning
文献类型：期刊论文
| 作者 | Xia, Yuehan2,3; Su, Yang3; Liu H(刘辉)1 ; Yu, Wenhui2,3; Li, Zhentong3; Chen, Wei3; Huang, Yu3; Gan, Weiqun3
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| 刊名 | SOLAR PHYSICS
 |
| 出版日期 | 2024-11 |
| 卷号 | 299期号:11 |
| 关键词 | X-ray bursts Solar flares Machine learning Hard X-ray imaging |
| ISSN号 | 0038-0938 |
| DOI | 10.1007/s11207-024-02399-4 |
| 产权排序 | 第3完成单位 |
| 文献子类 | Article |
| 英文摘要 | Most solar hard X-ray (HXR) imagers in the past and current solar missions obtain X-ray images via Fourier transform imaging technology, which requires proper imaging algorithms to reconstruct images from spatially-modulated or temporally-modulated signals. A variety of algorithms have been developed during the last 50 years for the characteristics of respective instruments. In this work, we present a new imaging algorithm developed based on deep learning for the Hard X-ray Imager (HXI) onboard the Advanced Space-based Solar Observatory (ASO-S) and the preliminary test results of the algorithm with both simulated data and observations. We first created a training dataset by obtaining modulation data from simulated HXR images of single, double and loop-shaped sources, respectively, and the patterns of HXI sub-collimators. Then, we introduced machine-learning algorithm to develop a pattern-based deep learning network model: HXI_DLA, which can directly produce an image from modulation counts. After training the model with simple sources, we tested DLA for simple sources, extended sources, and double sources for imaging dynamic range. Finally, we compared CLEAN and DLA images reconstructed from HXI observations of three flares. Overall, these imaging tests revealed that the current HXI_DLA method produces comparable image result to those from the widely used imaging method CLEAN. In some cases, DLA images are even slightly better. Besides, HXI_DLA is super fast for imaging and parameter-free. Although this is only the first step towards a fully developed and practical DLA method, the tests have shown the potential of deep learning in the field of solar hard X-ray imaging. |
| 学科主题 | 天文学 ; 太阳与太阳系 |
| URL标识 | 查看原文 |
| 出版地 | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS |
| WOS关键词 | ACCELERATION ; PIXON |
| 资助项目 | National Key R&D Program of China[2022YFF0503002]; National Natural Science Foundation of China (NSFC)[12333010]; National Natural Science Foundation of China (NSFC)[11873027]; National Natural Science Foundation of China (NSFC)[U2031140]; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0560000]; Strategic Priority Research Program on Space Science; Chinese Academy of Sciences[XDA15320000] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001355183400001 |
| 出版者 | SPRINGER |
| 资助机构 | National Key R&D Program of China[2022YFF0503002] ; National Natural Science Foundation of China (NSFC)[12333010, 11873027, U2031140] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0560000] ; Strategic Priority Research Program on Space Science ; Chinese Academy of Sciences[XDA15320000] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/27681]  |
| 专题 | 天文技术实验室 |
| 作者单位 | 1.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650216, People’s Republic of China 2.School of Astronomy and Space Science, University of Science and Technology of China, Hefei, 230026, People’s Republic of China; 3.Key Laboratory of Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023, Nanjing, China; |
| 推荐引用方式 GB/T 7714 | Xia, Yuehan,Su, Yang,Liu H,et al. A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning[J]. SOLAR PHYSICS,2024,299(11). |
| APA | Xia, Yuehan.,Su, Yang.,刘辉.,Yu, Wenhui.,Li, Zhentong.,...&Gan, Weiqun.(2024).A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning.SOLAR PHYSICS,299(11). |
| MLA | Xia, Yuehan,et al."A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning".SOLAR PHYSICS 299.11(2024). |
入库方式： OAI收割
来源：云南天文台
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