Pareto Optimization of a Laser Wakefield Accelerator
This work addresses optimization problems in accelerator physics, particularly for applications like light sources, but is incremental as it applies known optimization methods to this domain.
The researchers tackled the challenge of optimizing laser wakefield accelerator performance by using multi-objective Bayesian optimization to efficiently map the solution space, revealing a wide range of Pareto-optimal solutions that trade beam energy versus charge at similar efficiency and showing a direct trade-off between energy spread and efficiency when targeting specific energies.
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.