Identifying laguerre-gaussian modes using convolutional neural network
Document Type
Conference Proceeding
Publication Date
12-1-2019
Abstract
An automated determination of Laguerre-Gaussian (LG) modes benefits cavity tuning and optical communication. In this paper, we employ machine learning techniques to automatically detect the lowest sixteen LG modes of a laser beam. Convolutional neural networks (CNN) are trained by collecting the experimental and simulated datasets of LG modes that relies only on the intensity images of their unique patterns. We demonstrate that the trained CNN model can detect LG modes with the maximum accuracy greater than 96% after 60 epochs. The study evaluates the CNN's ability to generalize to new data and adapt to experimental conditions.
Publication Source (Journal or Book title)
Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
First Page
475
Last Page
478
Recommended Citation
Sharifi, S., Brown, S., Novikova, I., Mikhailov, E., Veronis, G., Dowling, J., Banadaki, Y., Siddiqui, E., Cuzzo, S., Bhusal, N., Cohen, L., Kalasky, A., Prajapati, N., & Soto-Garcia, R. (2019). Identifying laguerre-gaussian modes using convolutional neural network. Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 475-478. https://doi.org/10.1109/ICMLA.2019.00088
