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Automation and control of laser wakefield accelerators using Bayesian optimization

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Automation and control of laser wakefield accelerators using Bayesian optimization. / Shalloo, R. J.; Dann, S. J.D.; Gruse, J. N. et al.
In: Nature Communications, Vol. 11, No. 1, 6355, 11.12.2020.

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

Harvard

Shalloo, RJ, Dann, SJD, Gruse, JN, Underwood, CID, Antoine, AF, Arran, C, Backhouse, M, Baird, CD, Balcazar, MD, Bourgeois, N, Cardarelli, JA, Hatfield, P, Kang, J, Krushelnick, K, Mangles, SPD, Murphy, CD, Lu, N, Osterhoff, J, Põder, K, Rajeev, PP, Ridgers, CP, Rozario, S, Selwood, MP, Shahani, AJ, Symes, DR, Thomas, AGR, Thornton, C, Najmudin, Z & Streeter, MJV 2020, 'Automation and control of laser wakefield accelerators using Bayesian optimization', Nature Communications, vol. 11, no. 1, 6355. https://doi.org/10.1038/s41467-020-20245-6

APA

Shalloo, R. J., Dann, S. J. D., Gruse, J. N., Underwood, C. I. D., Antoine, A. F., Arran, C., Backhouse, M., Baird, C. D., Balcazar, M. D., Bourgeois, N., Cardarelli, J. A., Hatfield, P., Kang, J., Krushelnick, K., Mangles, S. P. D., Murphy, C. D., Lu, N., Osterhoff, J., Põder, K., ... Streeter, M. J. V. (2020). Automation and control of laser wakefield accelerators using Bayesian optimization. Nature Communications, 11(1), Article 6355. https://doi.org/10.1038/s41467-020-20245-6

Vancouver

Shalloo RJ, Dann SJD, Gruse JN, Underwood CID, Antoine AF, Arran C et al. Automation and control of laser wakefield accelerators using Bayesian optimization. Nature Communications. 2020 Dec 11;11(1):6355. doi: 10.1038/s41467-020-20245-6

Author

Shalloo, R. J. ; Dann, S. J.D. ; Gruse, J. N. et al. / Automation and control of laser wakefield accelerators using Bayesian optimization. In: Nature Communications. 2020 ; Vol. 11, No. 1.

Bibtex

@article{6e73062fb55946568c0a11acde7f1072,
title = "Automation and control of laser wakefield accelerators using Bayesian optimization",
abstract = "Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.",
author = "Shalloo, {R. J.} and Dann, {S. J.D.} and Gruse, {J. N.} and Underwood, {C. I.D.} and Antoine, {A. F.} and C. Arran and M. Backhouse and Baird, {C. D.} and Balcazar, {M. D.} and N. Bourgeois and Cardarelli, {J. A.} and P. Hatfield and J. Kang and K. Krushelnick and Mangles, {S. P.D.} and Murphy, {C. D.} and N. Lu and J. Osterhoff and K. P{\~o}der and Rajeev, {P. P.} and Ridgers, {C. P.} and S. Rozario and Selwood, {M. P.} and Shahani, {A. J.} and Symes, {D. R.} and Thomas, {A. G.R.} and C. Thornton and Z. Najmudin and Streeter, {M. J.V.}",
year = "2020",
month = dec,
day = "11",
doi = "10.1038/s41467-020-20245-6",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Automation and control of laser wakefield accelerators using Bayesian optimization

AU - Shalloo, R. J.

AU - Dann, S. J.D.

AU - Gruse, J. N.

AU - Underwood, C. I.D.

AU - Antoine, A. F.

AU - Arran, C.

AU - Backhouse, M.

AU - Baird, C. D.

AU - Balcazar, M. D.

AU - Bourgeois, N.

AU - Cardarelli, J. A.

AU - Hatfield, P.

AU - Kang, J.

AU - Krushelnick, K.

AU - Mangles, S. P.D.

AU - Murphy, C. D.

AU - Lu, N.

AU - Osterhoff, J.

AU - Põder, K.

AU - Rajeev, P. P.

AU - Ridgers, C. P.

AU - Rozario, S.

AU - Selwood, M. P.

AU - Shahani, A. J.

AU - Symes, D. R.

AU - Thomas, A. G.R.

AU - Thornton, C.

AU - Najmudin, Z.

AU - Streeter, M. J.V.

PY - 2020/12/11

Y1 - 2020/12/11

N2 - Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

AB - Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

U2 - 10.1038/s41467-020-20245-6

DO - 10.1038/s41467-020-20245-6

M3 - Journal article

C2 - 33311487

AN - SCOPUS:85097486626

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 6355

ER -