Abstract
The rapid and accurate identification of large-scale orbital angular momentum (OAM) modes is crucial for expanding the application of vortex beams (VBs). In this paper, an OAM mode recognition method based on convolutional neural networks (CNNs) is proposed and investigated. We construct an 8-layer CNN possesses complex feature extraction capability and train it to own powerful anti-turbulence competence by feeding the intensity patterns of VBs interfered by Gaussian beam. After supervised training of a large sample set, the CNN model takes on excellent network generalization ability and can well detect VBs with the mode range of [−50,50]. The simulation results indicate that under the influence of weak and medium turbulences, the average recognition accuracy exceeds 99%. Even under strong turbulence, the accuracy also reaches 98.54%. Meanwhile, the identification time is only 1.55ms per OAM mode with Intel(R) Xeon(R) Gold 6148 CPU. Moreover, the influence of different Gaussian beam waists, VB orders, input training sets, and CNN structures on OAM mode recognition performance, is fully studied. These results demonstrate that our proposed method can achieve higher accuracy and higher order OAM mode detection at a fast speed, which contributes a more effective method for the recognition of VBs.
Highlights
Vortex beams (VBs), a structured light, have helical phase wave-front [1]
A Gaussian beam is used to interfere with VBs to obtain the interference patterns containing conjugated orbital angular momentum (OAM) mode information
The simulation results display that the average recognition accuracy rate exceeds 99.49% for 100 OAM modes in the range of [−50, 50], and the average time to recognize per OAM mode is only 1.55ms
Summary
Vortex beams (VBs), a structured light, have helical phase wave-front [1]. Its spiral phase can be represented by exp(ilφ), and per photon carries an orbital angular momentum (OAM) of l, where l shows the topological charge characterizing the OAM mode, is the Planck constant divided by 2π, and φ denotes the azimuth [2]–[4]. The associate editor coordinating the review of this manuscript and approving it for publication was Rajeeb Dey. nonuniform light field distributions reward VBs many unique optical properties, which have attracted research interests in the fields of nonlinear optics [9], optical communication [10], [11], high-precision optical measurement [12], [13] and optical manipulation [14], etc. Nonuniform light field distributions reward VBs many unique optical properties, which have attracted research interests in the fields of nonlinear optics [9], optical communication [10], [11], high-precision optical measurement [12], [13] and optical manipulation [14], etc In these fields, the effective recognition of OAM modes is one of the most challenges in VB applications.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
