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From the same journal

Automation and control of laser wakefield accelerators using Bayesian optimisation

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Author(s)

  • R. J. Shalloo
  • S. J.D. Dann
  • J. N. Gruse
  • C. I.D. Underwood
  • A. F. Antoine
  • M. Backhouse
  • C. D. Baird
  • M. D. Balcazar
  • N. Bourgeois
  • J. A. Cardarelli
  • P. Hatfield
  • J. Kang
  • K. Krushelnick
  • S. P.D. Mangles
  • N. Lu
  • J. Osterhoff
  • K. Põder
  • P. P. Rajeev
  • S. Rozario
  • M. P. Selwood
  • A. J. Shahani
  • D. R. Symes
  • A. G.R. Thomas
  • C. Thornton
  • Z. Najmudin
  • M. J.V. Streeter

Department/unit(s)

Publication details

JournalNature Communications
DateAccepted/In press - 20 Nov 2020
DatePublished (current) - 11 Dec 2020
Issue number1
Volume11
Number of pages8
Original languageEnglish

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%.

Bibliographical note

© The Author(s) 2020

    Research areas

  • physics.acc-ph, physics.plasm-ph

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