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

TL;DR

This paper introduces a deep learning method for rapid and accurate modal decomposition of Hermite-Gaussian beams from single-shot intensity images, enhancing optical field analysis and applications.

Contribution

It is the first to apply deep learning for HG beam modal decomposition, providing a fast, cost-effective, and robust approach for optical field characterization.

Findings

Enables single-shot phase and power content extraction

Offers a fast and economical alternative to traditional methods

Improves robustness and accuracy in HG beam analysis

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.