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 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.展开更多
基金support from the UK STFC grants ST/V001639/1 with the XFEL Physical Sciences Hub and ST/P002021/1the UK EPSRC grants EP/V049577/1 and EP/R006202/1+5 种基金as well as the U.S.DOE Office of Science,Fusion Energy Sciences under FWP No.100182in part by the National Science Foundation under Grant No.1632708 and Award No.PHY–1903414M.J.V.S.acknowledges support from the Royal Society URFR1221874support from the DOE NNSA SSGF program under DE-NA0003960support from the U.S.DOE grant DESC0016804support from the project‘Advanced research using high-intensity laser-produced photons and particles’(CZ.02.1.01/0.0/0.0/16_019/0000789)from the European Regional Development Fund(ADONIS)。
文摘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.
