Optimization of accelerator performance parameters is limited by numerous trade-offs and finding the appropriate balance between optimization goals for an unknown system is challenging to achieve. Here we show that multi-objective Bayesian optimization can map the solution space of a laser wakefield accelerator in a very sample-efficient way. Using a Gaussian mixture model, we isolate contributions related to an electron bunch at a certain energy and we observe that there exists a wide range of Pareto-optimal solutions that trade beam energy versus charge at similar laser-to-beam efficiency. However, many applications such as light sources require particle beams at a certain target energy. Once such a constraint is introduced we observe a direct trade-off between energy spread and accelerator efficiency. We furthermore demonstrate how specific solutions can be exploited using \emph{a posteriori} scalarization of the objectives, thereby efficiently splitting the exploration and exploitation phases.

翻译：加速器性能参数的优化受限于众多折中关系，对于未知系统而言，在优化目标之间寻找合适的平衡点极具挑战性。本文展示多目标贝叶斯优化能够以极高样本效率映射激光尾场加速器的解空间。通过采用高斯混合模型，我们分离了特定能量电子束团的贡献，发现存在一系列帕累托最优解，这些解在相似激光-电子束效率条件下权衡了束流能量与电荷量。然而，诸如光源等许多应用需要特定目标能量的粒子束。引入此类约束后，我们观察到能量发散度与加速器效率之间存在直接折中关系。此外，我们进一步论证如何通过目标的后验标量化开发特定解，从而有效分离探索与利用阶段。