Pareto Optimization of a Laser Wakefield Accelerator

• URL: http://arxiv.org/abs/2303.15825v1
• Date: Tue, 28 Mar 2023 08:54:29 GMT
• Title: Pareto Optimization of a Laser Wakefield Accelerator
• Authors: F. Irshad, C. Eberle, F.M. Foerster, K. v. Grafenstein, F. Haberstroh, E. Travac, N. Weisse, S. Karsch, and A. D\"opp
• Abstract summary: We show that multi-objective Bayesian optimization can map the solution space of a laser wakefield accelerator in a very sample-efficient way. We demonstrate how specific solutions can be exploited using empha posteriori scalarization of the objectives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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