A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning

doi:10.1007/s11207-024-02399-4

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	A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning
	Xia, Yuehan 1,2; Su, Yang 1; Liu H(刘辉)3 ; Yu, Wenhui 1,2; Li, Zhentong 1; Chen, Wei 1; Huang, Yu 1; Gan, Weiqun 1
发表期刊	SOLAR PHYSICS
	2024-11
卷号	299 期号:11
DOI	10.1007/s11207-024-02399-4
产权排序	第3完成单位
收录类别	SCI
关键词	X-ray bursts Solar flares Machine learning Hard X-ray imaging
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.
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]
项目资助者	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]
语种	英语
学科领域	天文学 ; 太阳与太阳系
文章类型	Article
出版者	SPRINGER
出版地	VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
ISSN	0038-0938
URL	查看原文
WOS记录号	WOS:001355183400001
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
关键词[WOS]	ACCELERATION ; PIXON
引用统计
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.ynao.ac.cn/handle/114a53/27681
专题	天文技术实验室
作者单位	1.Key Laboratory of Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023, Nanjing, China; 2.School of Astronomy and Space Science, University of Science and Technology of China, Hefei, 230026, People’s Republic of China; 3.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650216, People’s Republic of 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).

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