# Hermite-Gaussian Mode Detection via Convolution Neural Networks

arXiv: https://arxiv.org/abs/1904.00239 · HTML: https://mlsanity.com/paper/1904.00239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Deep learning for enhanced free-space optical communications](https://mlsanity.com/paper/2208.07712/) (2022)

## Abstract (arXiv)

Hermite-Gaussian (HG) laser modes are a complete set of solutions to the free-space paraxial wave equation in Cartesian coordinates and represent a close approximation to physically-realizable laser cavity modes. Additionally, HG modes can be mode-multiplexed to significantly increase the information capacity of optical communication systems due to their orthogonality. Since, both cavity tuning and optical communication applications benefit from a machine vision determination of HG modes, convolution neural networks were implemented to detect the lowest twenty-one unique HG modes with an accuracy greater than 99%. As the effectiveness of a CNN is dependent on the diversity of its training data, extensive simulated and experimental datasets were created for training, validation and testing.