Rapid Throughout of an MeV Ultrafast Electron Diffraction Instrument System

ORAL

Abstract

MeV ultrafast electron diffraction is a powerful structural measurement technique for novel characterization of matter. We investigate the demonstration of realtime or near-realtime data processing enabled by data science/machine learning/artificial intelligence in conjunctions with high-performance computing to facilitate automated operation, data acquisition and processing.

*Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, Program of Electron and Scanning Probe Microscopies, award DE-SC0021365. Funding available through the Department of Energy's Established Program to Stimulate Competitive Research (EPSCoR) State-National Laboratory Partnerships program in the Office of Basic Energy and Sciences. This research used resources of the Brookhaven National Laboratory's Accelerator Test Facility and of the Argonne Leadership Computing Facility, which are DOE Office of Science User Facilities.

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Authors

  • Mariana Fazio

    • Department of Electrical and Computer Engineering, University of New Mexico

  • S. Biedron

    • Department of Electrical and Computer Engineering and Department of Mechanical Engineering, University of New Mexico

  • D. Kirby

    • Department of Electrical and Computer Engineering, University of New Mexico

  • D. Monk

    • Department of Electrical and Computer Engineering, University of New Mexico

  • M. Martínez-Ramón

    • Department of Electrical and Computer Engineering, University of New Mexico

  • S. Sosa

    • Department of Electrical and Computer Engineering, University of New Mexico

  • D. Martins

    • Argonne National Laboratory

  • M. Papka

    • Argonne National Laboratory

  • M. Babzien

    • Brookhaven National Laboratory

  • K. Brown

    • Brookhaven National Laboratory

  • M. Palmer

    • Brookhaven National Laboratory

  • J. Tao

    • Brookhaven National Laboratory

  • A. Hurd

    • Los Alamos National Laboratory

  • J. Chen

    • Los Alamos National Laboratory

  • R. Prasankumar

    • Los Alamos National Laboratory

  • J. Sarrao

    • Los Alamos National Laboratory

  • C. Sweeney

    • Los Alamos National Laboratory