Particle accelerators require constant tuning during operation to meet beam quality, total charge and particle energy requirements for use in a wide variety of physics, chemistry and biology experiments. Maximizing the performance of an accelerator facility often necessitates multiobjective optimization, where operators must balance trade-offs between multiple competing objectives simultaneously, often using limited, temporally expensive beam observations. Usually, accelerator optimization problems are solved off-line, prior to actual operation, with advanced beam line simulations and parallelized optimization methods (NSGA-II, swarm optimization). Unfortunately, it is not feasible to use these methods for online multiobjective optimization, since beam measurements can only be done in a serial fashion, and these optimization methods require a large number of measurements to converge to a useful solution. Here, we introduce a multiobjective Bayesian optimization scheme, which finds the full Pareto front of an accelerator optimization problem efficiently in a serialized manner and is thus a critical step towards practical online multiobjective optimization in accelerators. This method uses a set of Gaussian process surrogate models, along with a multiobjective acquisition function, to reduce the number of observations needed to converge by at least an order of magnitude over current methods. We demonstrate how this method can be modified to specifically solve optimization challenges posed by the tuning of accelerators. This includes the addition of optimization constraints, objective preferences and costs related to changing accelerator parameters.

中文翻译：

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用于在线加速器调整的多目标贝叶斯优化

粒子加速器在运行过程中需要不断调整，以满足光束质量、总电荷和粒子能量要求，以用于各种物理、化学和生物实验。最大化加速器设施的性能通常需要多目标优化，其中操作员必须同时平衡多个竞争目标之间的权衡，通常使用有限的、时间上昂贵的波束观测。通常，加速器优化问题是在实际操作之前离线解决的，使用先进的光束线模拟和并行优化方法（NSGA-II，群优化）。不幸的是，将这些方法用于在线多目标优化是不可行的，因为光束测量只能以串行方式进行，而这些优化方法需要大量的测量才能收敛到一个有用的解决方案。在这里，我们介绍了一种多目标贝叶斯优化方案，它以序列化的方式有效地找到加速器优化问题的完整帕累托前沿，因此是加速器中实用在线多目标优化的关键一步。该方法使用一组高斯过程替代模型以及多目标采集函数，以将收敛所需的观测次数减少至少一个数量级，而不是当前方法。我们演示了如何修改此方法以专门解决由加速器调整带来的优化挑战。这包括添加优化约束，