Energy-optimized scaling laws for self-guided laser wakefield accelerators

Abstract

Laser wakefield acceleration promises compact electron accelerators for applications in medicine, industry, and fundamental science. Yet, despite rapid progress, accurately predicting the electron energy attainable in a given experimental configuration and the acceleration length required to reach it remains an open challenge. Here we use Bayesian optimization combined with advanced particle-in-cell simulation techniques to determine the maximum electron energy that a self-guided laser wakefield accelerator driven by a laser of a given energy and wavelength can produce. By systematically optimizing the accelerator performance across a range of laser energies and wavelengths, we derive energy-optimized scaling laws. These scaling laws yield the highest electron energy over the shortest acceleration length possible, are expressed solely in terms of laser energy and wavelength, and are accompanied by the complete set of laser and plasma parameters required to enable the scaling. The resulting scaling laws provide practical guidance for designing state-of-the-art laser wakefield acceleration experiments operating at their fundamental performance limits.