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URLhttps://doi.org/10.18429/JACoW-IPAC2024-TUPS76
TitleMachine learning for data analysis and control of an MeV ultrafast electron diffraction system and a photocathode laser and gun system: updates
Authors
  • T. Bolin, A. Aslam, M. Martinez-Ramon, S. Biedron
    University of New Mexico
  • M. Babzien, M. Palmer, M. Fedurin, R. Malone, W. Li
    Brookhaven National Laboratory
AbstractAn MeV ultrafast electron diffraction (MUED) in-strument system is a unique characterization technique used to study ultrafast processes in a variety of mate-rials by a pump-probe method. Combining this tech-nology with rapid data science and artificial intelli-gence/machine learning (AI/ML) technologies in con-junction with high-performance computing can create a turnkey, automated instrument. AI-based system controls can also provide real-time electron beam optimization or provide virtual diagnostics of the beamline operational parameters. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations Such a data-science-enabled MUED facility will open this technique to a wider user base with a wider variation of experience, providing an automated or semi-automated state-of-the-art instrument, with a beamline scientist orchestrating the overall data collection pro-cess. Updates on research and development efforts pri-marily in the realm of initial studies of network con-nection between the ALCF and the Accelerator Test Facility (ATF) at Brookhaven National Laboratory are presented.
Paperdownload: TUPS76.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference15th International Particle Accelerator Conference
Series
LocationNashville, TN
Date19-24 May 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardFulvia Pilat - Oak Ridge National Laboratory Wolfram Fischer - Brookhaven National Laboratory Robert Saethre - Oak Ridge National Laboratory Petr Anisimov - Los Alamos National Laboratory Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A.
Online ISBN978-3-95450-247-9
Online ISSN2673-5490
Received15 May 2024
Revised23 May 2024
Accepted23 May 2024
Issued01 July 2024
DOI10.18429/JACoW-IPAC2024-TUPS76
Pages1858-1861