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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