Dual-Output Mode Analysis of Multimode Laguerre-Gaussian Beams via Deep Learning

Abstract

The Laguerre-Gaussian (LG) beam demonstrates great potential for optical communication due to its orthogonality between different eigenstates, and has gained increased research interest in recent years. Here, we propose a dual-output mode analysis method based on deep learning that can accurately obtain both the mode weight and phase information of multimode LG beams. We reconstruct the LG beams based on the result predicted by the convolutional neural network. It shows that the correlation coefficient values after reconstruction are above 0.9999, and the mean absolute error (MAE) of the mode weights and phases are about 1.4 × 10−3 and 2.9 × 10−3, respectively. The model still maintains relatively accurate prediction for the associated unknown data set and the noise-disturbed samples. In addition, the computation time of the model for a single test sample takes only 0.975 ms on average. These results show that our method has good abilities of generalization and robustness and allows for nearly real-time modal analysis.

Keywords

• mode analysis
• orbital angular momentum
• Laguerre-Gaussian beam
• convolutional neural network
• deep learning