Automated control and optimization of laser-driven ion acceleration

B. Loughran; M. J. V. Streeter; H. Ahmed; S. Astbury; M. Balcazar; M. Borghesi; N. Bourgeois; C. B. Curry; S. J. D. Dann; S. DiIorio; N. P. Dover; T. Dzelzanis; O. C. Ettlinger; M. Gauthier; L. Giuffrida; G. D. Glenn; S. H. Glenzer; J. S. Green; R. J. Gray; G. S. Hicks; C. Hyland; V. Istokskaia; M. King; D. Margarone; O. McCusker; P. McKenna; Z. Najmudin; C. Parisuaña; P. Parsons; C. Spindloe; D. R. Symes; A. G. R. Thomas; F. Treffert; N. Xu; C. A. J. Palmer

doi:10.1017/hpl.2023.23

Automated control and optimization of laser-driven ion acceleration

B. Loughran, M. J. V. Streeter, H. Ahmed, S. Astbury, M. Balcazar, M. Borghesi, N. Bourgeois, C. B. Curry, S. J. D. Dann, S. DiIorio, N. P. Dover, T. Dzelzanis, O. C. Ettlinger, M. Gauthier, L. Giuffrida, G. D. Glenn, S. H. Glenzer, J. S. Green, R. J. Gray, G. S. HicksC. Hyland, V. Istokskaia, M. King, D. Margarone, O. McCusker, P. McKenna, Z. Najmudin, C. Parisuaña, P. Parsons, C. Spindloe, D. R. Symes, A. G. R. Thomas, F. Treffert, N. Xu, C. A. J. Palmer

Physics

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Abstract

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

Original language	English
Article number	e35
Number of pages	9
Journal	High Power Laser Science and Engineering
Volume	11
Early online date	27 Mar 2023
DOIs	https://doi.org/10.1017/hpl.2023.23
Publication status	Published - 15 May 2023

Keywords

Bayesian optimisation
high repetition rate laser-target interaction
laser-driven particle acceleration
proton generation

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Loughran, B., Streeter, M. J. V., Ahmed, H., Astbury, S., Balcazar, M., Borghesi, M., Bourgeois, N., Curry, C. B., Dann, S. J. D., DiIorio, S., Dover, N. P., Dzelzanis, T., Ettlinger, O. C., Gauthier, M., Giuffrida, L., Glenn, G. D., Glenzer, S. H., Green, J. S., Gray, R. J., ... Palmer, C. A. J. (2023). Automated control and optimization of laser-driven ion acceleration. High Power Laser Science and Engineering, 11, [e35]. https://doi.org/10.1017/hpl.2023.23

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title = "Automated control and optimization of laser-driven ion acceleration",

abstract = "The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.",

keywords = "Bayesian optimisation, high repetition rate laser-target interaction, laser-driven particle acceleration, proton generation",

author = "B. Loughran and Streeter, {M. J. V.} and H. Ahmed and S. Astbury and M. Balcazar and M. Borghesi and N. Bourgeois and Curry, {C. B.} and Dann, {S. J. D.} and S. DiIorio and Dover, {N. P.} and T. Dzelzanis and Ettlinger, {O. C.} and M. Gauthier and L. Giuffrida and Glenn, {G. D.} and Glenzer, {S. H.} and Green, {J. S.} and Gray, {R. J.} and Hicks, {G. S.} and C. Hyland and V. Istokskaia and M. King and D. Margarone and O. McCusker and P. McKenna and Z. Najmudin and C. Parisua{\~n}a and P. Parsons and C. Spindloe and Symes, {D. R.} and Thomas, {A. G. R.} and F. Treffert and N. Xu and Palmer, {C. A. J.}",

year = "2023",

month = may,

day = "15",

doi = "10.1017/hpl.2023.23",

language = "English",

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Loughran, B, Streeter, MJV, Ahmed, H, Astbury, S, Balcazar, M, Borghesi, M, Bourgeois, N, Curry, CB, Dann, SJD, DiIorio, S, Dover, NP, Dzelzanis, T, Ettlinger, OC, Gauthier, M, Giuffrida, L, Glenn, GD, Glenzer, SH, Green, JS, Gray, RJ, Hicks, GS, Hyland, C, Istokskaia, V, King, M, Margarone, D, McCusker, O, McKenna, P, Najmudin, Z, Parisuaña, C, Parsons, P, Spindloe, C, Symes, DR, Thomas, AGR, Treffert, F, Xu, N & Palmer, CAJ 2023, 'Automated control and optimization of laser-driven ion acceleration', High Power Laser Science and Engineering, vol. 11, e35. https://doi.org/10.1017/hpl.2023.23

TY - JOUR

T1 - Automated control and optimization of laser-driven ion acceleration

AU - Loughran, B.

AU - Streeter, M. J. V.

AU - Ahmed, H.

AU - Astbury, S.

AU - Balcazar, M.

AU - Borghesi, M.

AU - Bourgeois, N.

AU - Curry, C. B.

AU - Dann, S. J. D.

AU - DiIorio, S.

AU - Dover, N. P.

AU - Dzelzanis, T.

AU - Ettlinger, O. C.

AU - Gauthier, M.

AU - Giuffrida, L.

AU - Glenn, G. D.

AU - Glenzer, S. H.

AU - Green, J. S.

AU - Gray, R. J.

AU - Hicks, G. S.

AU - Hyland, C.

AU - Istokskaia, V.

AU - King, M.

AU - Margarone, D.

AU - McCusker, O.

AU - McKenna, P.

AU - Najmudin, Z.

AU - Parisuaña, C.

AU - Parsons, P.

AU - Spindloe, C.

AU - Symes, D. R.

AU - Thomas, A. G. R.

AU - Treffert, F.

AU - Xu, N.

AU - Palmer, C. A. J.

PY - 2023/5/15

Y1 - 2023/5/15

N2 - The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

AB - The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

KW - Bayesian optimisation

KW - high repetition rate laser-target interaction

KW - laser-driven particle acceleration

KW - proton generation

U2 - 10.1017/hpl.2023.23

DO - 10.1017/hpl.2023.23

M3 - Article

AN - SCOPUS:85151549946

VL - 11

M1 - e35

ER -

Automated control and optimization of laser-driven ion acceleration

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Keywords

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