Machine learning pattern recognition algorithm with applications to coherent laser combination

We analyze a new kind of machine learning algorithm designed to feedback stabilize coherently combined lasers. This algorithm learns differential, rather than absolute, values of action in phase space, in order to facilitate learning on initially unstable systems. Experiments have shown that this ap...

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Bibliographic Details
Published in:IEEE journal of quantum electronics Vol. 58; no. 6; p. 1
Main Authors: Wang, Dan, Du, Qiang, Zhou, Tong, Gilardi, Antonio, Kiran, Mariam, Mohammed, Bashir, Li, Derun, Wilcox, Russell
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerospace electronics
Algorithms
Artificial neural networks
Coherent Beam Combination
Control
Laser beams
Machine learning
MATHEMATICS AND COMPUTING
Measurement by laser beam
Neural Network
Pattern recognition
Phase Drift
Photonic Signal Processing
Stability analysis
Training
ISSN:0018-9197, 1558-1713
Online Access:Get full text
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Summary:We analyze a new kind of machine learning algorithm designed to feedback stabilize coherently combined lasers. This algorithm learns differential, rather than absolute, values of action in phase space, in order to facilitate learning on initially unstable systems. Experiments have shown that this approach can control small-scale spatial beam combination with high stability. In this paper we analyze the algorithm's performance and limitations in depth, showing that it can continuously learn during operation in order to track changes. Using simulation, we extend the application to temporal combination, and show that it scales to more complex instances by combining 81 beams.
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content type line 14
AC02-05CH11231
USDOE Office of Science (SC), High Energy Physics (HEP)
ISSN:0018-9197
1558-1713
DOI:10.1109/JQE.2022.3204437