论文标题
使用贝叶斯优化调整具有安全限制的粒子加速器
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
论文作者
论文摘要
粒子加速器的调谐计算机参数是一项重复且耗时的任务，可自动化。尽管可以使用许多现成的优化算法，但实际上，它们的使用受到限制，因为大多数方法都不考虑每种迭代中的安全至关重要的约束，例如损失信号或步进尺寸的限制。一个值得注意的例外是安全的贝叶斯优化，这是通过嘈杂的反馈进行数据驱动的调整方法。我们建议并评估保罗·施雷尔学院（PSI）的两个研究设施的安全贝叶斯优化的阶梯尺寸有限变体：a）瑞士游离电子激光器（SwissFel）和b）高强度质子加速器（HIPA）。我们报告了两台机器上有希望的实验结果，最多调整了16个受约束约束的参数。
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most methods do not account for safety-critical constraints in each iteration, such as loss signals or step-size limitations. One notable exception is safe Bayesian optimization, which is a data-driven tuning approach for global optimization with noisy feedback. We propose and evaluate a step-size limited variant of safe Bayesian optimization on two research facilities of the Paul Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) the High-Intensity Proton Accelerator (HIPA). We report promising experimental results on both machines, tuning up to 16 parameters subject to 224 constraints.
