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Using artificial neural networks for open-loop tomography

Osborn, James; De Cos Juez, Francisco Javier; Guzman, Dani; Butterley, Timothy; Myers, Richard; Guesalaga, Andrés; Laine, Jesus

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Authors

Francisco Javier De Cos Juez

Dani Guzman

Timothy Butterley

Richard Myers

Andrés Guesalaga

Jesus Laine



Abstract

Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques.

Citation

Osborn, J., De Cos Juez, F. J., Guzman, D., Butterley, T., Myers, R., Guesalaga, A., & Laine, J. (2012). Using artificial neural networks for open-loop tomography. Optics Express, 20(3), 2420-2434. https://doi.org/10.1364/oe.20.002420

Journal Article Type Article
Publication Date Jan 30, 2012
Deposit Date Jan 30, 2012
Publicly Available Date Mar 29, 2024
Journal Optics Express
Publisher Optica
Peer Reviewed Peer Reviewed
Volume 20
Issue 3
Pages 2420-2434
DOI https://doi.org/10.1364/oe.20.002420

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Copyright Statement
© 2012 The Optical Society. This paper was published in Optics express and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: https://doi.org/10.1364/OE.20.002420. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.





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