Author: Palmer, M.A.
Paper Title Page
MOPOPT057 Updates in Efforts to Data Science Enabled MeV Ultrafast Electron Diffraction System 397
 
  • S. Biedron, T.B. Bolin, M. Martínez-Ramón, S.I. Sosa Guitron
    UNM-ECE, Albuquerque, USA
  • M. Babzien, M.G. Fedurin, J.J. Li, M.A. Palmer
    BNL, Upton, New York, USA
  • S. Biedron
    UNM-ME, Albuquerque, New Mexico, USA
  • D. Martin, M.E. Papka
    ANL, Lemont, Illinois, USA
 
  Funding: Work supported by DOEs EPSCoR award DE-SC0021365, used resources of the Brookhaven National Laboratory’s Accelerator Test Facility and of the Argonne Leadership Computing Facility.
MeV ultrafast electron diffraction (MUED) is a pump-probe characterization technique to study ultrafast phenomena in materials with high temporal and spatial resolution. This complex instrument can be advanced into a turn-key, high-throughput tool with the aid of machine learning (ML) mechanisms and high-performance computing. The MUED instrument at the Accelerator Test Facility in Brookhaven National Laboratory was employed to test different ML approaches for both data analysis and control. We characterized different materials using MUED, mainly polycrystalline gold and single crystal Ta2NiS5. Diffraction patterns were acquired in single shot mode and convolutional neural network autoenconder models were evaluated for noise reduction and the reconstruction error was studied to identify anomalous diffraction patterns. Electron beam energy jitter was analyzed from single shot diffraction patterns to be used as a novel diagnostic tool. The MUED beamline was also simulated using VSim to construct a surrogate model for control of beam shape and energy. Progress towards ML-based controls leveraging off Argonne Leadership Computing Facility resources will also be presented.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT057  
About • Received ※ 02 July 2022 — Accepted ※ 26 June 2022 — Issue date ※ 08 July 2022  
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