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

Abstract 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...

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Autores principales:	Zhe Zhang, Xi Yang, Xiaobiao Huang, Junjie Li, Timur Shaftan, Victor Smaluk, Minghao Song, Weishi Wan, Lijun Wu, Yimei Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a8e49568fa624eb3aa76fcf10c35690d
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spelling	oai:doaj.org-article:a8e49568fa624eb3aa76fcf10c35690d2021-12-02T16:15:05ZAccurate prediction of mega-electron-volt electron beam properties from UED using machine learning10.1038/s41598-021-93341-22045-2322https://doaj.org/article/a8e49568fa624eb3aa76fcf10c35690d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93341-2https://doaj.org/toc/2045-2322Abstract 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.Zhe ZhangXi YangXiaobiao HuangJunjie LiTimur ShaftanVictor SmalukMinghao SongWeishi WanLijun WuYimei ZhuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhe Zhang Xi Yang Xiaobiao Huang Junjie Li Timur Shaftan Victor Smaluk Minghao Song Weishi Wan Lijun Wu Yimei Zhu Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
description	Abstract 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.
format	article
author	Zhe Zhang Xi Yang Xiaobiao Huang Junjie Li Timur Shaftan Victor Smaluk Minghao Song Weishi Wan Lijun Wu Yimei Zhu
author_facet	Zhe Zhang Xi Yang Xiaobiao Huang Junjie Li Timur Shaftan Victor Smaluk Minghao Song Weishi Wan Lijun Wu Yimei Zhu
author_sort	Zhe Zhang
title	Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
title_short	Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
title_full	Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
title_fullStr	Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
title_full_unstemmed	Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
title_sort	accurate prediction of mega-electron-volt electron beam properties from ued using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a8e49568fa624eb3aa76fcf10c35690d
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Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

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