Laser Wakefield accelerator modelling with variational neural networks

Tools

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 (https://doi.org/10.1017/hpl.2022.47)

[thumbnail of Streeter-etal-HPLSE-2022-Laser-Wakefield-accelerator-modelling-with-variational]
Preview
 Text. Filename: Streeter_etal_HPLSE_2022_Laser_Wakefield_accelerator_modelling_with_variational.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

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.

ORCID iDs

Streeter, M.J.V., Colgan, C., Cobo, C.C., Arran, C., Los, E.E., Watt, R., Bourgeois, N., Calvin, L., Carderelli, J., Cavanagh, N., Dann, S J.D., Fitzgarrald, R., Gerstmayr, E., Joglekar, A.S., Kettle, B., Mckenna, P. ORCID logoORCID: https://orcid.org/0000-0001-8061-7091, Murphy, C.D., Najmudin, Z., Parsons, P., Qian, Q., Rajeev, P.P., Ridgers, C.P., Symes, D.R., Thomas, A.G.R., Sarri, G. and Mangles, S.P.D.;
  • Item type:Article
    ID code:83897
    Dates:
    Date
    Event
    6 January 2023
    Published
    6 January 2023
    Published Online
    7 December 2022
    Accepted
    Subjects:Science > Physics > Optics. Light
    Department:Faculty of Science > Physics
    Depositing user: Pure Administrator
    Date deposited:27 Jan 2023 11:13
    Last modified:13 Dec 2024 20:01
    URI:https://strathprints.strath.ac.uk/id/eprint/83897
CORE (COnnecting REpositories)