Machine learning applied to fiber-fed focal plane wavefront sensing: a study of aberrated wave transmission through multimode optical fibers
Date Issued
2024
Author(s)
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Harris, Robert J.
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Abstract
This research explores the potential of machine learning and neural networks in recognizing the input features of aberrated wavefronts transmitted through multimode optical fibers, in view of applications for wavefront sensing in ground-based telescopes. Recent studies highlight the efficacy of multimode fibers for imaging and sensing, suggesting neural networks' effectiveness in mapping relationships between output distortions and input wavefront aberrations. The initial step of our study concerned multimode fiber propagation simulations. An input Gaussian beam was distorted with known aberrations and then sent through the fiber to analyze the effects on the output. This groundwork was used to train and validate a Convolutional Neural Network architecture. Its main role was to understand, from output images, which type of aberration was superimposed in input. We obtained promising results with test accuracy of 85% and 87%, while achieving good performance in network training and generalization.
Coverage
Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI
All editors
Navarro, Ramón; Jedamzik, Ralf
Series
Volume
13100
Start page
131006D
Conferenece
Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI
Conferenece place
Yokohama, Japan
Conferenece date
16-22 June, 2024
Issn Identifier
0277-786X
Ads BibCode
2024SPIE13100E..6DD
Rights
open.access
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