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    Classifying orbital angular momentum (OAM) modes is crucial for optical communications. A new interferometry method accurately distinguishes OAM modes, even in turbulent conditions, offering a low-cost solution.

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    Area of Science:

    • Optics and Photonics
    • Machine Learning Applications

    Background:

    • Orbital angular momentum (OAM) modes are versatile and used in free-space optical communications.
    • Classifying OAM modes is essential, but intensity-based methods struggle with similar mode patterns.
    • OAM mode phase is unique, suggesting phase-sensitive techniques are needed for accurate classification.

    Purpose of the Study:

    • To develop and validate a high-accuracy method for classifying OAM modes.
    • To investigate the efficacy of interferometry combined with deep learning for OAM mode classification.
    • To assess the performance of a shear interferometer for OAM mode discrimination in turbulent environments.

    Main Methods:

    • Utilized deep learning, specifically convolutional neural networks, for OAM mode classification.
    • Employed interferometry techniques, including a shear interferometer and a Mach-Zehnder interferometer.
    • Tested the classification accuracy of OAM modes under simulated turbulent conditions.

    Main Results:

    • Achieved very high classification accuracy for a range of OAM modes in turbulence.
    • Demonstrated that a shear interferometer, without a reference beam, is highly effective.
    • Showed comparable, marginally higher accuracy with a Mach-Zehnder interferometer.

    Conclusions:

    • Interferometry combined with deep learning offers a robust solution for OAM mode classification.
    • The shear interferometer technique is a promising candidate for real-time, low-cost modal decomposition.
    • This approach overcomes limitations of intensity-only methods for OAM mode identification.