Identifying Laguerre-Gaussian Modes using Convolutional Neural Network

Abstract

An automated determination of Laguerre-Gaussian (LG) modes benefits cavity tuning and optical communication. In this paper, we employ machine learning techniques to automatically detect the lowest sixteen LG modes of a laser beam. Convolutional neural networks (CNN) are trained by collecting the experimental and simulated datasets of LG modes that relies only on the intensity images of their unique patterns. We demonstrate that the trained CNN model can detect LG modes with the maximum accuracy greater than 96% after 60 epochs. The study evaluates the CNN's ability to generalize to new data and adapt to experimental conditions.