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arXiv · 1907.06081

Preliminary study on the modal decomposition of Hermite Gaussian beams via deep learning

Yi An·Tianyue Hou·Jun Li·Liangjin Huang·Jinyong Leng·Lijia Yang·Pu Zhou
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Abstract

The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to our knowledge, deep learning-based modal decomposition (MD) of HG beams. This method offers a fast, economical and robust way to acquire both the power content and phase information through a single-shot beam intensity image, which will be beneficial for the beam shaping, beam quality assessment, studies of resonator perturbations, and other further research on the HG beams.

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Yi An, Tianyue Hou, Jun Li, Liangjin Huang, Jinyong Leng, Lijia Yang, Pu Zhou. 2019-07-17. Preliminary study on the modal decomposition of Hermite Gaussian beams via deep learning. https://arxiv.org/abs/1907.06081

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