Laser wakefield accelerator modelling with variational neural networks

M. J. V. Streeter*, C. Colgan, C. C. Cobo, C. Arran, E. E. Los, R. Watt, N. Bourgeois, L. Calvin, J. Carderelli, N. Cavanagh, S. J. D. Dann, R. Fitzgarrald, E. Gerstmayr, A. S. Joglekar, B. Kettle, P. Mckenna, C. D. Murphy, Z. Najmudin, P. Parsons, Q. QianP. P. Rajeev, C. P. Ridgers, D. R. Symes, A. G. R. Thomas, G. Sarri, S. P. D. Mangles

*Corresponding author for this work

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A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.

Original languageEnglish
Article numbere9
Number of pages8
JournalHigh Power Laser Science and Engineering
Publication statusPublished - 06 Jan 2023

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering


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