Generation Of A Hybrid Angular Momentum Beam And Its Angular Momentum Detection

Due to its unique physical properties,vortex beam has been widely studied and applied in many fields in recent years.The unique property of vortex beam carrying orbital angular momentum(OAM)makes it suitable for optical communication.OAM,as a medium for information transmission,has the advantages of orthogonality and infinite representation range.However,due to the complexity of physical devices and the disturbance of atmospheric turbulence,the recognition accuracy and transmission efficiency of the traditional free-space OAM transmission system are not high.Therefore,we use machine learning to help detect and recognize OAM by image recognition method of light intensity distribution.In this paper,we propose and generate a beam with hybrid angular momentum,which has great advantages in the recognition of OAM and provides more controllable degrees of freedom for the recognition of OAM.Based on this hybrid angular momentum beam,we realized high accuracy recognition of optical orbital angular momentum based on petal interference mode by using machine learning scheme of convolutional neural network(CNN)with improved AlexNet architecture.The relationship of the training sample resolution and number associated with the accuracy and the training time of the model is presented.The recognition accuracy is closely related with the quantum number l of OAM,the angular ratio n of the spire phase over the hybrid phase in one modulation period,and the propagation distance z.Our studies show that when l ranges from 1 to 10,and n varies from 0.02 to 0.99,the recognition accuracy of OAM is nearly 100%.The minimum interval of n recognized at the OAM modes decreases to 0.01,which as far as we know is the first time this super-high bandwidth has been realized.Such results suggest a great potential application for the next generation CNN based OAM optical communication.