Author: Maheshwari, M.
Paper Title Page
WEPV020 Learning to Lase: Machine Learning Prediction of FEL Beam Properties 677
  • A.E. Pollard, D.J. Dunning
    STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
  • M. Maheshwari
    STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
  Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications.  
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About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 28 December 2021       Issue date ※ 25 February 2022
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