Automated control and optimization of laser-driven ion acceleration
Automated control and optimization of laser-driven ion acceleration
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DOI：
10.1017/hpl.2023.23
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发表时间：
2023-03-27
影响因子：
4.8
通讯作者：
Palmer，C. A. J.
中科院分区：
文献类型：
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作者：
Loughran，B.;Streeter，M. J. V.;Palmer，C. A. J.
相对论强激光与不透明靶的相互作用是一个高度非线性的多维参数空间。这限制了用于优化二次辐射的实验参数的连续1D扫描的效用，尽管由于低数据采集速率，迄今为止这已经是公认的方法。通过机器学习增强的高重复率（HRR）激光器为有效的光源优化提供了宝贵的机会。在这里，一个自动化的，HRR兼容的系统产生了高保真度的参数扫描，揭示了激光强度对靶预热和质子产生的影响。最大质子能量的闭环贝叶斯优化，通过控制激光波前和目标位置，产生的质子束具有与手动优化的激光脉冲相同的最大能量，但仅使用60%的激光能量。激光驱动质子束的自动优化演示是迈向更深入的物理洞察和未来辐射源建设的关键一步。
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.