Dual-Output Mode Analysis of Multimode Laguerre-Gaussian Beams via Deep Learning
Dual-Output Mode Analysis of Multimode Laguerre-Gaussian Beams via Deep Learning
复制标题
通过深度学习对多模拉盖尔高斯光束进行双输出模态分析
作者：
Xudong Yuan;Yaguang Xu;Ruizhi Zhao;Xuhao Hong;Ronger Lu;Xia Feng;Yongchuang Chen;Jincheng Zou;Chao Zhang;Yiqiang Qin;Yongyuan Zhu
拉盖尔-高斯光束由于其不同本征态之间的正交性，在光通信中显示出巨大的潜力，近年来得到了越来越多的研究兴趣。在此，我们提出了一种基于深度学习的双输出模式分析方法，可以准确地获得多模LG光束的模式权重和相位信息。我们在卷积神经网络预测结果的基础上重建了LG光束。结果表明，重构后的相关系数值均在0.9999以上，模态权值和相位的平均绝对误差（MAE）分别约为1.4 ×10−3和2.9 ×10−3。对于相关的未知数据集和受噪声干扰的样本，该模型仍然保持相对准确的预测。此外，该模型对单个测试样本的计算时间平均仅为0.975 ms。结果表明，该方法具有良好的泛化能力和鲁棒性，可以实现近乎实时的模态分析。
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.