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

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A machine learning model was created to predict the electron spectrum generated by a GeVclass 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 on that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior undergoing any process which 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
Early online date6 Jan 2023
Publication statusE-pub ahead of print - 6 Jan 2023


  • laser Wakefield accelerator model
  • variational neural networks
  • machine learning model
  • convolutional neural networks


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