Physics-informed Bayesian optimization of high-order harmonic generation in gases

Abstract

Optimization of high-order harmonic generation in gases is essential for attosecond science. Improving conversion efficiency from the infrared driving laser to the extreme-ultraviolet high-order harmonic radiation requires balancing microscopic single-atom dipole responses with macroscopic phase matching. The resulting conversion-efficiency landscape exhibits complex structures, such as the hyperbolic ridge in the pressure-length space, which can be difficult to accurately reconstruct using conventional Bayesian optimization. Here, we introduce a physics-informed Bayesian optimization framework that incorporates known physical scaling relations through nonlinear transformations of the search space. We first demonstrate the approach in the two-dimensional pressure-length space, where a direct grid scan enables detailed validation of the reconstructed landscape, before extending it to four dimensions, where exhaustive scanning becomes computationally impractical. In four dimensions, incorporating the pulse-duration scaling and a square-root transformation of conversion efficiency further improves the accuracy of the reconstructed landscape, as validated against an independent reference dataset. We also employ a threshold-based acquisition strategy to identify extended high-efficiency operating regions rather than isolated optimal points. Together, these results demonstrate that incorporating physical knowledge into Bayesian optimization improves the exploration and reconstruction of multidimensional high-order harmonic generation landscapes, extending its capabilities beyond locating individual optima.

arXiv comment18 pages, 7 figures