Bayesian optimization of electron energy from laser wakefield accelerators

TL;DR

This paper employs Bayesian optimization combined with 3D particle-in-cell simulations to identify optimal laser and plasma parameters that maximize electron energy in laser wakefield accelerators, providing insights into scalability.

Contribution

It introduces a novel approach integrating Bayesian optimization with simulations and derives analytical expressions for maximum electron energy considering laser effects.

Findings

Optimal parameters identified for maximum electron energy.

Analytical expressions for energy and acceleration distance derived.

Discussion on scalability of accelerator parameters.

Abstract

We use Bayesian optimization in combination with three-dimensional particle-in-cell simulations to determine the optimal laser and plasma parameters that, for a given laser pulse energy, maximize the cut-off energy of an electron beam produced by laser wakefield accelerators. We assume a Gaussian laser driver with matched spot size and amplitude and investigate both self-guiding in a uniform-density plasma and guiding in a preformed plasma channel with matched radius. To interpret the simulation results quantitatively, we derive novel analytical expressions for the maximum electron energy and the corresponding acceleration distance, accounting for the effects of laser diffraction and energy depletion. Based on the results obtained, we discuss the potential scalability of the optimal input (plasma density, pulse duration, amplitude, spot size, and channel radius) and output (electron energy, electron charge, acceleration length, and acceleration efficiency) parameters to laser systems of arbitrary energy.