采用贝叶斯优化的laser尾流场加速器的自动化和控制
Automation and control of laser wakefield accelerators using Bayesian optimisation
摘要：
激光韦克菲尔德加速器有望彻底改变加速器科学的许多领域。然而，其广泛采用的最大挑战之一是由于输入参数和加速结构的动态演变之间的耦合而难以控制和优化加速器输出。在这里，我们使用机器学习技术来自动化100 MeV规模的加速器，该加速器通过同时改变多达6个参数来优化其输出，包括激光的光谱和空间相位以及等离子体密度和长度。最值得注意的是，该算法建立的模型能够优化激光演化，否则可能会在单变量扫描中错过。激光脉冲形状的细微调整导致电子束电荷增加80%，尽管脉冲长度仅改变1%。
英文摘要：
Laser wakefield accelerators promise to revolutionise many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimisation of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimised its outputs by simultaneously varying up to 6 parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimisation of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.
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