Pareto Optimization and Tuning of a Laser Wakefield Accelerator

Abstract

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 multiobjective Bayesian optimization can map the solution space of a laser wakefield accelerator (LWFA) in a very sample-efficient way. We observe that there exists a wide range of Pareto-optimal solutions that trade beam energy versus charge at similar laser-to-beam efficiency. Moreover, many applications such as light sources require particle beams at certain target energies. We demonstrate accurate energy tuning of the LWFA from 150 to 400 MeV via the simultaneous adjustment of eight parameters. To further advance this use case, we propose an inverse model that allows a user to specify desired beam parameters. Trained on the forward Gaussian process model, the inverse model generates input parameter value ranges within which the desired setting is likely to be reached. The method reveals different strategies for accelerator tuning and is expected to drastically facilitate the operation of LWFAs in the near future.