Optimized matching conditions for self-guided laser wakefield accelerators

TL;DR

This paper refines the matching conditions for self-guided laser wakefield accelerators using Bayesian optimization and simulations, enabling higher electron energies with greater operational flexibility.

Contribution

It introduces an optimized formulation of matching conditions and demonstrates their effectiveness through simulations, reducing the need for precise parameter tuning.

Findings

Maximum electron energy identified for given laser energy.

Electrons near maximum energy achievable over wide parameter ranges.

Relaxed operational constraints for laser wakefield accelerators.

Abstract

We revisit the matching conditions for self-guided laser pulse propagation in plasma and refine their formulation to maximize the energy of electrons produced via laser wakefield acceleration. Bayesian optimization, combined with particle-in-cell simulations carried out in a quasi-three-dimensional geometry and a Lorentz-boosted frame, is employed. The optimization identifies the maximum electron energy that a self-guided laser wakefield accelerator, driven by a laser of a given energy, can produce, together with the corresponding acceleration distance. Our results further demonstrate that electrons with energies close to the maximum value can be obtained across a relatively wide range of input parameters and without the need for their precise tuning. This provides substantial flexibility for experimental implementation and significantly relaxes the operational constraints associated with self-guided laser wakefield accelerators.