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Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems

González-Gutiérrez, Carlos; Santos, Jesús; Martínez-Zarzuela, Mario; Basden, Alistair; Osborn, James; Díaz-Pernas, Francisco; De Cos Juez, Francisco

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Authors

Carlos González-Gutiérrez

Jesús Santos

Mario Martínez-Zarzuela

Francisco Díaz-Pernas

Francisco De Cos Juez



Abstract

Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

Citation

González-Gutiérrez, C., Santos, J., Martínez-Zarzuela, M., Basden, A., Osborn, J., Díaz-Pernas, F., & De Cos Juez, F. (2017). Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems. Sensors, 17(6), Article 1263. https://doi.org/10.3390/s17061263

Journal Article Type Article
Acceptance Date May 30, 2017
Online Publication Date Jun 2, 2017
Publication Date Jun 2, 2017
Deposit Date Jul 6, 2017
Publicly Available Date Mar 29, 2024
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 17
Issue 6
Article Number 1263
DOI https://doi.org/10.3390/s17061263

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).





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