Machine-learning iterative optimization for all polarization-maintaining linear cavity Er:fiber laser

All polarization-maintaining (PM) linear cavity mode -locked fiber lasers are promising ultrafast laser sources due to their compactness and environmental robustness. Here, we demonstrate a linear cavity fiber laser with all-PM configuration experimentally and investigate the mode-locking formation of the laser using a machine-learning iterative optimization method based on the Gaussian process. The optimization algorithm can converge rapidly after only 30 runs. Using the optimized parameters, we simulate the generation of mode-locked pulses from noise. The out-put spectrum and pulse energy are highly consistent with the experiment. Furthermore, we describe the intracavity dynamic evolution under group velocity mismatch. We then show that the pulse trapping induced by cross-phase modulation leads to the overcompensated time synchronization between the orthogonally polarized components.(c) 2023 Optica Publishing Group

Automated summary

This article presents an experimental investigation on a polarization-maintaining linear cavity fiber laser and its mode-locking formation using a machine-learning optimization method. The dynamics of intracavity evolution and pulse trapping are also explored.