# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1907.06081