Machine learning based accurate recognition of fractional optical vortex modes in atmospheric environment

Abstract

Optical vortex beam with fractional orbital angular momentum (OAM) has great potential to increase the capacity of optical communication and information processing in classical and quantum regimes. However, atmospheric turbulence (AT) in free space distorts the helical phase-front of vortex beams and causes the mode diffusion, seriously hindering the practical application. Herein, using a convolutional neural network approach with an improved residual neural network architecture, we overcome the hurdle to give the accurate recognition of the fractional OAM in the AT. As demonstrated on the petal interference patterns, a type of hybrid beams carrying double OAM modes is utilized to provide two controllable degrees of freedom for greater recognition of more subtle OAM modes, e.g., the fractional topological charge number l and the angular ratio n. Our studies show that with various l and n, the recognition accuracy of OAM over 20 000 images is as high as 85.30% even under the strong AT parameter (Cn2 = 5 × 10−14 m−2/3) and the long propagation distance (z = 1500 m). Our findings represent a remarkable achievement toward highly accurate recognition of fractional OAM with broad bandwidth in the atmospheric environment, expanding the applications for the general interest of machine learning based OAM optical communication.