Recognition of Orbital Angular Momentum of Vortex Beams Based on Convolutional Neural Network and Multi-Objective Classifier

Abstract

Vortex beams carry orbital angular momentum (OAM), and their inherent infinite dimensional eigenstates can enhance the ability for optical communication and information processing in the classical and quantum fields. The measurement of the OAM of vortex beams is of great significance for optical communication applications based on vortex beams. Most of the existing measurement methods require the beam to have a regular spiral wavefront. Nevertheless, the wavefront of the light will be distorted when a vortex beam propagates through a random medium, hindering the accurate recognition of OAM by traditional methods. Deep learning offers a solution to identify the OAM of the vortex beam from a speckle field. However, the method based on deep learning usually requires a lot of data, while it is difficult to attain a large amount of data in some practical applications. To solve this problem, we design a framework based on convolutional neural network (CNN) and multi-objective classifier (MOC), by which the OAM of vortex beams can be identified with high accuracy using a small amount of data. We find that by combining CNN with different structures and MOC, the highest accuracy reaches 96.4%, validating the feasibility of the proposed scheme.

Keywords

• vortex beams
• orbital angular momentum
• speckle
• convolutional neural network
• multi-objective classifier