Duris J, Kennedy D, Hanuka A, Shtalenkova J, Edelen A, Baxevanis P, Egger A, Cope T, McIntire M, Ermon S, Ratner D
SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.
Department of Physics, University of California, Santa Cruz, Santa Cruz, California 95064, USA.
Phys Rev Lett. 2020 Mar 27;124(12):124801. doi: 10.1103/PhysRevLett.124.124801.
The Linac coherent light source x-ray free-electron laser is a complex scientific apparatus which changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing x-ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. The result is a sample-efficient optimization routine, combining both historical data and knowledge of accelerator physics to significantly outperform existing optimizers.
直线加速器相干光源X射线自由电子激光是一种复杂的科学仪器，每天会多次改变配置，因此需要快速调谐策略来减少连续实验的设置时间。为此，我们采用贝叶斯方法，通过控制四极磁铁组来最大化X射线激光脉冲能量。高斯过程模型提供了关于控制参数的机器响应的概率预测，从而在寻找全局最优值时实现探索和利用之间的平衡。我们表明，可以从存档扫描中学习模型参数，并且可以从束流传输中提取设备之间的相关性。结果是一个样本高效的优化程序，它结合了历史数据和加速器物理知识，显著优于现有的优化器。
