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Laser Wakefield Accelerator modelling with Variational Neural Networks

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Laser Wakefield Accelerator modelling with Variational Neural Networks. / Streeter, M. J.V.; Colgan, C.; Cobo, C. C. et al.
In: High Power Laser Science and Engineering, Vol. 11, e9, 06.01.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Streeter, MJV, Colgan, C, Cobo, CC, Arran, C, Los, EE, Watt, R, Bourgeois, N, Calvin, L, Carderelli, J, Cavanagh, N, Dann, SJD, Fitzgarrald, R, Gerstmayr, E, Joglekar, AS, Kettle, B, Mckenna, P, Murphy, CD, Najmudin, Z, Parsons, P, Qian, Q, Rajeev, PP, Ridgers, CP, Symes, DR, Thomas, AGR, Sarri, G & Mangles, SPD 2023, 'Laser Wakefield Accelerator modelling with Variational Neural Networks', High Power Laser Science and Engineering, vol. 11, e9. https://doi.org/10.1017/hpl.2022.47

APA

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., Murphy, C. D., Najmudin, Z., Parsons, P., ... Mangles, S. P. D. (2023). Laser Wakefield Accelerator modelling with Variational Neural Networks. High Power Laser Science and Engineering, 11, Article e9. https://doi.org/10.1017/hpl.2022.47

Vancouver

Streeter MJV, Colgan C, Cobo CC, Arran C, Los EE, Watt R et al. Laser Wakefield Accelerator modelling with Variational Neural Networks. High Power Laser Science and Engineering. 2023 Jan 6;11:e9. doi: 10.1017/hpl.2022.47

Author

Streeter, M. J.V. ; Colgan, C. ; Cobo, C. C. et al. / Laser Wakefield Accelerator modelling with Variational Neural Networks. In: High Power Laser Science and Engineering. 2023 ; Vol. 11.

Bibtex

@article{e68f71490f974e1b94ba98f365d472f8,
title = "Laser Wakefield Accelerator modelling with Variational Neural Networks",
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.",
author = "Streeter, {M. J.V.} and C. Colgan and Cobo, {C. C.} and C. Arran and Los, {E. E.} and R. Watt and N. Bourgeois and L. Calvin and J. Carderelli and N. Cavanagh and Dann, {S. J.D.} and R. Fitzgarrald and E. Gerstmayr and Joglekar, {A. S.} and B. Kettle and P. Mckenna and Murphy, {C. D.} and Z. Najmudin and P. Parsons and Q. Qian and Rajeev, {P. P.} and Ridgers, {C. P.} and Symes, {D. R.} and Thomas, {A. G.R.} and G. Sarri and Mangles, {S. P.D.}",
year = "2023",
month = jan,
day = "6",
doi = "10.1017/hpl.2022.47",
language = "English",
volume = "11",
journal = "High Power Laser Science and Engineering",
issn = "2052-3289",

}

RIS

TY - JOUR

T1 - Laser Wakefield Accelerator modelling with Variational Neural Networks

AU - Streeter, M. J.V.

AU - Colgan, C.

AU - Cobo, C. C.

AU - Arran, C.

AU - Los, E. E.

AU - Watt, R.

AU - Bourgeois, N.

AU - Calvin, L.

AU - Carderelli, J.

AU - Cavanagh, N.

AU - Dann, S. J.D.

AU - Fitzgarrald, R.

AU - Gerstmayr, E.

AU - Joglekar, A. S.

AU - Kettle, B.

AU - Mckenna, P.

AU - Murphy, C. D.

AU - Najmudin, Z.

AU - Parsons, P.

AU - Qian, Q.

AU - Rajeev, P. P.

AU - Ridgers, C. P.

AU - Symes, D. R.

AU - Thomas, A. G.R.

AU - Sarri, G.

AU - Mangles, S. P.D.

PY - 2023/1/6

Y1 - 2023/1/6

N2 - 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.

AB - 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.

U2 - 10.1017/hpl.2022.47

DO - 10.1017/hpl.2022.47

M3 - Journal article

AN - SCOPUS:85146162769

VL - 11

JO - High Power Laser Science and Engineering

JF - High Power Laser Science and Engineering

SN - 2052-3289

M1 - e9

ER -