# Pareto Optimization of a Laser Wakefield Accelerator

arXiv: https://arxiv.org/abs/2303.15825 · HTML: https://mlsanity.com/paper/2303.15825/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization](https://mlsanity.com/paper/1511.07827/) (2015)
- [Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization](https://mlsanity.com/paper/2111.14911/) (2021)
- [Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration](https://mlsanity.com/paper/2210.03484/) (2022)
- [Data-driven Science and Machine Learning Methods in Laser-Plasma Physics](https://mlsanity.com/paper/2212.00026/) (2022)

## Abstract (arXiv)

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 a posteriori scalarization of the objectives, thereby efficiently splitting the exploration and exploitation phases.