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arXiv · 1904.06735

Beam Profiler Network (BPNet) -- A Deep Learning Approach to Mode Demultiplexing of Laguerre-Gaussian Optical Beams

Amit Bekerman·Sahar Froim·Barak Hadad·Alon Bahabad
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Abstract

The transverse field profile of light is being recognized as a resource for classical and quantum communications for which reliable methods of sorting or demultiplexing spatial optical modes are required. Here, we demonstrate, experimentally, state-of-the-art mode demultiplexing of Laguerre-Gaussian beams according to both their orbital angular momentum and radial topological numbers using a flow of two concatenated deep neural networks. The first network serves as a transfer function from experimentally-generated to ideal numerically-generated data, while using a unique "Histogram Weighted Loss" function that solves the problem of images with limited significant information. The second network acts as a spatial-modes classifier. Our method uses only the intensity profile of modes or their superposition, making the phase information redundant.

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Amit Bekerman, Sahar Froim, Barak Hadad, Alon Bahabad. 2019-04-14. Beam Profiler Network (BPNet) -- A Deep Learning Approach to Mode Demultiplexing of Laguerre-Gaussian Optical Beams. https://doi.org/10.1364/ol.44.003629

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