تقرير
Pareto Optimization of a Laser Wakefield Accelerator
| العنوان: | Pareto Optimization of a Laser Wakefield Accelerator |
|---|---|
| المؤلفون: | Irshad, F., Eberle, C., Foerster, F. M., Grafenstein, K. v., Haberstroh, F., Travac, E., Weisse, N., Karsch, S., Döpp, A. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Accelerator Physics, Computer Science - Machine Learning, Physics - Plasma Physics |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2303.15825 |
| رقم الأكسشن: | edsarx.2303.15825 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |