Automated control and optimization of laser-driven ion acceleration

Loughran, B. and Streeter, M. J. V. and Ahmed, H. and Astbury, S. and Balcazar, M. and Borghesi, M. and Bourgeois, N. and Curry, C. B. and Dann, S. J. D. and DiIorio, S. and Dover, N. P. and Dzelzanis, T. and Ettlinger, O. C. and Gauthier, M. and Giuffrida, L. and Glenn, G. D. and Glenzer, S. H. and Green, J. S. and Gray, R. J. and Hicks, G. S. and Hyland, C. and Istokskaia, V. and King, M. and Margarone, D. and McCusker, O. and McKenna, P. and Najmudin, Z. and Parisuaña, C. and Parsons, P. and Spindloe, C. and Symes, D. R. and Thomas, A. G. R. and Treffert, F. and Xu, N. and Palmer, C. A. J. (2023) Automated control and optimization of laser-driven ion acceleration. High Power Laser Science and Engineering, 11. e35. ISSN 2052-3289 (https://doi.org/10.1017/hpl.2023.23)

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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.

  • Item type:Article
    ID code:85272
    Dates:
    Date
    Event
    15 May 2023
    Published
    27 March 2023
    Published Online
    24 February 2023
    Accepted
    Subjects:Science > Physics > Optics. Light
    Department:Faculty of Science > Physics
    Depositing user: Pure Administrator
    Date deposited:25 Apr 2023 11:24
    Last modified:25 Apr 2024 01:33
    URI:https://strathprints.strath.ac.uk/id/eprint/85272
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