Automated detection of orbital angular momentum (OAM) can tremendously contribute to quantum optical experiments. We develop convolutional neural networks to identify and classify noisy images of Laguerre–Gaussian (LG) modes collected from two different experimental set ups. We investigate the classification performance measures of the predictive classification models for experimental conditions. The results demonstrate accuracy and specificity above 90% in classifying 16 LG modes for both experimental set ups. However, the F-score, sensitivity, and precision of the classification range from 57% to 92%, depending on the number of imperfections in the images obtained from the experiments. This research could enhance the application of OAM light in telecommunications, sensing, and high-resolution imaging systems.
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