학술논문
Automated control and optimisation of laser driven ion acceleration
Document Type
Working Paper
Author
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.; Hicks, G. S.; Hyland, C.; Istokskaia, V.; King, M.; Margarone, D.; McCusker, O.; McKenna, P.; Najmudin, Z.; Parisuaña, C.; Parsons, P.; Spindloe, C.; Symes, D. R.; Thomas, A. G. R.; Treffert, F.; Xu, N.; Palmer, C. A. J.
Source
Subject
Language
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.
Comment: 11 pages
Comment: 11 pages