Streeter, M. J.V. and Colgan, C. and Cobo, C. C. and Arran, C. and Los, E. E. and Watt, R. and Bourgeois, N. and Calvin, L. and Carderelli, J. and Cavanagh, N. and Dann, S. J.D. and Fitzgarrald, R. and Gerstmayr, E. and Joglekar, A. S. and Kettle, B. and Mckenna, P. and Murphy, C. D. and Najmudin, Z. and Parsons, P. and Qian, Q. and Rajeev, P. P. and Ridgers, C. P. and Symes, D. R. and Thomas, A. G.R. and Sarri, G. and Mangles, S. P.D. (2023) Laser Wakefield Accelerator modelling with Variational Neural Networks. High Power Laser Science and Engineering, 11: e9. ISSN 2052-3289
Full text not available from this repository.Abstract
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.