Improved Bayesian optimization of laser-driven plasma surface high harmonic generation

Plasma surface high-order harmonics generation (SHHG) driven by intense laser pulses on plasma targets enables a high-quality extreme ultraviolet (EUV) source with high pulse energy and outstanding spatiotemporal coherence. Optimizing the performance of SHHG is important for its applications in single-shot imaging and EUV absorption spectroscopy. In this work, we demonstrate the optimization of laser-driven SHHG by an improved Bayesian strategy. A traditional Bayesian algorithm is first employed to optimize the SHHG intensity in a two-dimensional parameter space. 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 optimization. The improved Bayesian optimization approach is efficient and robust in convergent to a stable condition in multi-dimensionally optimizing the harmonic ellipticity and intensity. This paves the way for generating a high-quality coherent EUV source with a high repetition rate and promoting further EUV applications.