Laser-pulse characterization using strong-field autocorrelation patterns and random-forest-based machine learning

Abstract

Building on the strategy presented in Opt. Lett. 47, 3992 (2022), we demonstrate an efficient alternative approach for the in situ characterization of ultrashort low-frequency laser pulses. In this context, we employ first-principles quantum-mechanical calculations to model the strong-field ionization of rare-gas atoms and produce autocorrelation patterns for a set of few-femtosecond near-infrared laser pulses. We explore the nonperturbative and nonlinear dependence of the autocorrelation patterns on the pulse characteristics and postulate an analytical function describing these patterns. For every laser pulse considered, we employ the parameters appearing in this analytical function, together with the underlying pulse parameters for supervised machine learning. Specifically, we use the random-forest technique for retrieving key laser pulse parameters from autocorrelation patterns produced via strong-field ionization. The current approach offers advantages for application to experimental data.