Efficient optimization of plasma surface high harmonic generation by an improved Bayesian strategy

TL;DR

This paper presents an improved Bayesian optimization method for enhancing plasma surface high harmonic generation, effectively optimizing multiple parameters to improve harmonic ellipticity in high-dimensional space, aiding future experimental applications.

Contribution

The paper introduces a novel Bayesian optimization approach combining Latin hypercube sampling and dynamic acquisition to efficiently optimize high-dimensional parameters in plasma surface high harmonic generation.

Findings

The improved Bayesian strategy effectively optimizes harmonic ellipticity in three dimensions.

The method overcomes the curse of dimensionality and avoids local optima.

It demonstrates robustness and efficiency in simulation-based optimization.

Abstract

Plasma surface high-order harmonics generation (SHHG) driven by intense laser pulses on plasma targets enables a high-quality extreme ultraviolet source with high pulse energy and outstanding spatiotemporal coherence. Optimizing the performance of SHHG is important for its applications in single-shot imaging and absorption spectroscopy. In this work, we demonstrate the optimization of laser-driven SHHG by an improved Bayesian strategy in conjunction with particle-in-cell simulations. A traditional Bayesian algorithm is first employed to optimize the SHHG intensity in a two-dimensional space of parameter. Then an improved Bayesian strategy, using the Latin hypercube sampling technique and a dynamic acquisition strategy, is developed to overcome the curse of dimensionality and the risk of local optima in a high-dimensional space optimization. The improved Bayesian optimization approach is efficient and robust in three-dimensionally optimizing the harmonic ellipticity, paving the way for the upcoming SHHG experiments with a considerable repetition rate.