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. Qian; P. P. Rajeev; C. P. Ridgers; D. R. Symes; A. G.R. Thomas; G. Sarri; S. P.D. Mangles

doi:10.1017/hpl.2022.47

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

Physics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Journal	High Power Laser Science and Engineering
DOIs	https://doi.org/10.1017/hpl.2022.47
Publication status	Accepted/In press - 6 Jan 2023

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10.1017/hpl.2022.47Licence: CC BY

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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. (Accepted/In press). Laser Wakefield Accelerator modelling with Variational Neural Networks. High Power Laser Science and Engineering. https://doi.org/10.1017/hpl.2022.47

@article{43f5f95fe53d42f4b22b0e7d037a5a62,

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.}",

note = "{\textcopyright} The Author(s), 2023",

year = "2023",

month = jan,

day = "6",

doi = "10.1017/hpl.2022.47",

language = "English",

journal = "High Power Laser Science and Engineering",

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

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.

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DO - 10.1017/hpl.2022.47

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SN - 2095-4719

JO - High Power Laser Science and Engineering

JF - High Power Laser Science and Engineering

ER -

Laser Wakefield Accelerator modelling with Variational Neural Networks

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The new intensity frontier - exploring quantum electrodynamic plasmas

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Laser Wakefield Accelerator modelling with Variational Neural Networks

Abstract

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The new intensity frontier - exploring quantum electrodynamic plasmas

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