Automated control and optimisation 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 optimisation 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 optimisation. 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 optimisation 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 optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.
Cite
@article{arxiv.2303.00823,
title = {Automated control and optimisation of laser driven ion acceleration},
author = {B. Loughran and M. J. V. Streeter and H. Ahmed and S. Astbury and M. Balcazar and M. Borghesi and N. Bourgeois and C. B. Curry and S. J. D. Dann and S. DiIorio and N. P. Dover and T. Dzelzanis and O. C. Ettlinger and M. Gauthier and L. Giuffrida and G. D. Glenn and S. H. Glenzer and J. S. Green and R. J. Gray and G. S. Hicks and C. Hyland and V. Istokskaia and M. King and D. Margarone and O. McCusker and P. McKenna and Z. Najmudin and C. Parisuaña and P. Parsons and C. Spindloe and D. R. Symes and A. G. R. Thomas and F. Treffert and N. Xu and C. A. J. Palmer},
journal= {arXiv preprint arXiv:2303.00823},
year = {2023}
}
Comments
11 pages