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

Daria Kolbasova and Robin Santra
Phys. Rev. A 107, 013520 – Published 24 January 2023

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.

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  • Received 23 September 2022
  • Accepted 10 January 2023

DOI:https://doi.org/10.1103/PhysRevA.107.013520

©2023 American Physical Society

Physics Subject Headings (PhySH)

Nonlinear DynamicsAtomic, Molecular & Optical

Authors & Affiliations

Daria Kolbasova1,* and Robin Santra1,2

  • 1Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, 22607 Hamburg, Germany
  • 2Department of Physics, Universität Hamburg, 22607 Hamburg, Germany

  • *daria.kolbasova@desy.de

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Vol. 107, Iss. 1 — January 2023

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