Seeing the beam cleanup effect in a high-power graded-index-fiber Raman amplifier based on mode decomposition

Amplification of random lasing enables a 10-kW-level high-spectral-purity Yb-Raman fiber laser

By amplifying the cascaded random Raman fiber laser (RRFL) oscillator and ytterbium fiber laser oscillator, we present the first, to the best of our knowledge, demonstration of a 10-kW-level high-spectral-purity all-fiber ytterbium-Raman fiber amplifier (Yb-RFA). With a carefully designed backward-pumped RRFL oscillator structure, the parasitic oscillation between the cascaded seeds is avoided. Leveraging the RRFL with full-open-cavity as the Raman seed, the Yb-RFA realizes 10.7-kW Raman lasing at 1125 nm, which is beyond the operating wavelengths of all the reflection components used in the system. The spectral purity of the Raman lasing reaches 94.7% and the 3-dB bandwidth is 3.9 nm. This work paves a way to combine the temporal stability of the RRFL seed and the power scaling of Yb-RFA, enabling the wavelength extension of high-power fiber lasers with high spectral purity.

High-performance mode decomposition using physics- and data-driven deep learning

A novel physics- and data-driven deep-learning (PDDL) method is proposed to execute complete mode decomposition (MD) for few-mode fibers (FMFs). The PDDL scheme underlies using the embedded beam propagation model of FMF to guide the neural network (NN) to learn the essential physical features and eliminate unexpected features that conflict with the physical laws. It can greatly enhance the NN's robustness, adaptability, and generalization ability in MD. In the case of obtaining the real modal weights (ρ2) and relative phases (θ), the PDDL method is investigated both in theory and experiment. Numerical results show that the PDDL scheme eliminates the generalization defect of traditional DL-based MD and the error fluctuation is alleviated. Compared with the DL-based MD, in the 8-mode case, the errors of ρ2 and θ can be reduced by 12 times and 100 times for beam patterns that differ greatly from the training dataset. Moreover, the PDDL maintains high accuracy even in the 8-mode MD case with a practical maximum noise factor of 0.12. In terms of adaptation, with a large variation of the core radius and NA of the FMF, the error keeps lower than 0.43% and 2.08% for ρ2 and θ, respectively without regenerating new dataset and retraining NN. The experimental configuration is set up and verifies the accuracy of the PDDL-based MD. Results show that the correlation factor of the real and reconstructed beam patterns is higher than 98%. The proposed MD-scheme shows much potential in the application of practical modal coupling characterization and laser beam quality analysis.

Modal decomposition of an incoherent combined laser beam based on the combination of residual networks and a stochastic parallel gradient descent algorithm

With the increase of the superimposed eigenmodes number, the traditional numerical modal decomposition (MD) technique will inevitably suffer from ambiguity and local minima problems and thus is typically unsuitable for conducting modal decomposition of an incoherent combined laser beam. In this paper, we propose a novel, to the best of our knowledge, MD algorithm, named ResNet-SPGD, which combines the advantages of residual networks (ResNet) and stochastic parallel gradient descent (SPGD) algorithm. Via setting the modal mode coefficients obtained from the CNN model as the initial value of the SPGD algorithm, such algorithm shows an attractive solution to mitigate the problem of modal ambiguity. The proposed algorithm is preliminarily applied to the modal decomposition of an incoherent combined laser beam, and the feasibility is demonstrated via numerical simulations. Complete MD is performed with high accuracy, and the only cost is the sacrifice of some real-time capacity.