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Title: 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 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.

Authors:
 [1];  [2];  [3];  [2];  [2];  [2];  [1];  [4];  [2]; ORCiD logo [2]
+ Show Author Affiliations
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Argonne National Lab. (ANL), Lemont, IL (United States)
  4. ShanghaiTech Univ. (China)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Brookhaven National Lab. (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1819831
Alternate Identifier(s):
OSTI ID: 1818937; OSTI ID: 1844540
Report Number(s):
BNL-222062-2021-JAAM
Journal ID: ISSN 2045-2322; TRN: US2214185
Grant/Contract Number:  
SC0012704; AC02-76SF00515; AC02-06CH11357; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; Condensed-matter physics; Experimental particle physics; UED using machine learning; mega-electron-volt electron beam

Citation Formats

Zhang, Zhe, Yang, Xi, Huang, Xiaobiao, Li, Junjie, Shaftan, Timur, Smaluk, Victor, Song, Minghao, Wan, Weishi, Wu, Lijun, and Zhu, Yimei. Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning. United States: N. p., 2021. Web. doi:10.1038/s41598-021-93341-2.
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Zhang, Zhe, Yang, Xi, Huang, Xiaobiao, Li, Junjie, Shaftan, Timur, Smaluk, Victor, Song, Minghao, Wan, Weishi, Wu, Lijun, & Zhu, Yimei. Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning. United States. https://doi.org/10.1038/s41598-021-93341-2
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Zhang, Zhe, Yang, Xi, Huang, Xiaobiao, Li, Junjie, Shaftan, Timur, Smaluk, Victor, Song, Minghao, Wan, Weishi, Wu, Lijun, and Zhu, Yimei. Tue . "Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning". United States. https://doi.org/10.1038/s41598-021-93341-2. https://www.osti.gov/servlets/purl/1819831.
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@article{osti_1819831,
title = {Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning},
author = {Zhang, Zhe and Yang, Xi and Huang, Xiaobiao and Li, Junjie and Shaftan, Timur and Smaluk, Victor and Song, Minghao and Wan, Weishi and Wu, Lijun and Zhu, Yimei},
abstractNote = {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.},
doi = {10.1038/s41598-021-93341-2},
journal = {Scientific Reports},
number = 1,
volume = 11,
place = {United States},
year = {Tue Jul 06 00:00:00 EDT 2021},
month = {Tue Jul 06 00:00:00 EDT 2021}
}
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https://doi.org/10.1038/s41598-021-93341-2
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