High-resolution recognition of low-coherence fractional OAM modes with deep learning-based methods

This study investigates high-resolution recognition of the topological charge (TC) in partially coherent fractional vortex beams. The goal is to achieve accurate TC detection with an orbital angular momentum (OAM) mode interval as small as 0.01 using DenseNet-based deep learning frameworks. The proposed approach analyzes the cross-spectral density (CSD) function distribution, achieving recognition accuracy of up to 99.99%, which represents a significant improvement over intensity-based methods. Simulated applications were conducted in free-space optical transmission systems for image transfer. These simulations leveraged the unique correlation structure of the CSD as a second-order statistical parameter for encoding information. The results confirmed nearly perfect recognition accuracy, underscoring the method's potential to enhance both communication capacity and security.