Bayesian Optimization of Laser-Wakefield Acceleration via Spectral Pulse Shaping

TL;DR

This paper employs Bayesian optimization with particle-in-cell simulations to enhance laser-plasma accelerator performance through spectral pulse shaping, achieving higher charge and energy in electron bunches.

Contribution

It introduces a novel application of spectral pulse shaping combined with Bayesian optimization to improve laser-wakefield acceleration efficiency.

Findings

Spectral shaping increases electron bunch charge by an order of magnitude.

Optimized laser profiles lead to higher mean electron energies.

The approach identifies high-performing configurations in complex parameter space.

Abstract

In this paper, we investigate the effect of spectral pulse shaping of the laser driver on the performance of channel-guided, laser-plasma accelerators. The study was carried out with the assistance of Bayesian optimization using particle-in-cell simulations. We used a realistic plasma profile based on a novel optical-field-ionized channel technique with ionization injection and low on-axis plasma densities to maximize the energy gain of the electron bunch trailing the laser. Spectral shaping allows us to modify the temporal profile of the laser driver while keeping the laser energy constant, affecting the acceleration and injection processes. Given the complexity and breadth of the parameter space in question, we used numerical optimization to identify high performers. In particular, we found laser profiles with additional spectral content that, when used with optimal plasma channel parameters, result in charge content an order of magnitude higher than the baseline Gaussian case while also increasing the mean energy of the electron bunch.