Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

doi:10.1038/s41598-021-93341-2

	Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
	Zhang, Zhe 1; Yang, Xi 2; Huang, Xiaobiao 3; Li, Junjie 4; Shaftan, Timur 2; Smaluk, Victor 2; Song, Minghao 1; Wan, Weishi5 ; Wu, Lijun 4; Zhu, Yimei 4
	2021-07-06
发表期刊	SCIENTIFIC REPORTS (IF:3.8[JCR-2023],4.3[5-Year])
ISSN	2045-2322
卷号	11 期号:1
DOI	10.1038/s41598-021-93341-2
摘要	To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.
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收录类别	SCIE
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000672717300001
出版者	NATURE PORTFOLIO
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/127938
专题	物质科学与技术学院_外聘教师
通讯作者	Yang, Xi
作者单位	1.SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA; 2.Brookhaven Natl Lab, Natl Synchrotron Light Source 2, Upton, NY 11973 USA; 3.Argonne Natl Lab, Adv Photon Source, Lemont, IL 60439 USA; 4.Brookhaven Natl Lab, Condensed Matter Phys & Mat Sci Div, Upton, NY 11973 USA; 5.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zhe,Yang, Xi,Huang, Xiaobiao,et al. Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning[J]. SCIENTIFIC REPORTS,2021,11(1).
APA	Zhang, Zhe.,Yang, Xi.,Huang, Xiaobiao.,Li, Junjie.,Shaftan, Timur.,...&Zhu, Yimei.(2021).Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning.SCIENTIFIC REPORTS,11(1).
MLA	Zhang, Zhe,et al."Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning".SCIENTIFIC REPORTS 11.1(2021).