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Automation and control of laser wakefield accelerators using Bayesian optimization

Author

Listed:
  • R. J. Shalloo

    (Imperial College London)

  • S. J. D. Dann

    (STFC Rutherford Appleton Laboratory)

  • J.-N. Gruse

    (Imperial College London)

  • C. I. D. Underwood

    (York Plasma Institute, University of York)

  • A. F. Antoine

    (University of Michigan)

  • C. Arran

    (York Plasma Institute, University of York)

  • M. Backhouse

    (Imperial College London)

  • C. D. Baird

    (STFC Rutherford Appleton Laboratory
    York Plasma Institute, University of York)

  • M. D. Balcazar

    (University of Michigan)

  • N. Bourgeois

    (STFC Rutherford Appleton Laboratory)

  • J. A. Cardarelli

    (University of Michigan)

  • P. Hatfield

    (University of Oxford)

  • J. Kang

    (University of Michigan)

  • K. Krushelnick

    (University of Michigan)

  • S. P. D. Mangles

    (Imperial College London)

  • C. D. Murphy

    (York Plasma Institute, University of York)

  • N. Lu

    (University of Michigan)

  • J. Osterhoff

    (Deutsches Elektronen-Synchrotron DESY)

  • K. Põder

    (Deutsches Elektronen-Synchrotron DESY)

  • P. P. Rajeev

    (STFC Rutherford Appleton Laboratory)

  • C. P. Ridgers

    (York Plasma Institute, University of York)

  • S. Rozario

    (Imperial College London)

  • M. P. Selwood

    (York Plasma Institute, University of York)

  • A. J. Shahani

    (University of Michigan)

  • D. R. Symes

    (STFC Rutherford Appleton Laboratory)

  • A. G. R. Thomas

    (University of Michigan)

  • C. Thornton

    (STFC Rutherford Appleton Laboratory)

  • Z. Najmudin

    (Imperial College London)

  • M. J. V. Streeter

    (Imperial College London)

Abstract

Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

Suggested Citation

  • R. J. Shalloo & S. J. D. Dann & J.-N. Gruse & C. I. D. Underwood & A. F. Antoine & C. Arran & M. Backhouse & C. D. Baird & M. D. Balcazar & N. Bourgeois & J. A. Cardarelli & P. Hatfield & J. Kang & K., 2020. "Automation and control of laser wakefield accelerators using Bayesian optimization," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-20245-6
    DOI: 10.1038/s41467-020-20245-6
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