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Using artificial neural networks for open-loop tomography.

Osborn, James and De Cos Juez, Francisco Javier and Guzman, Dani and Butterley, Timothy and Myers, Richard and Guesalaga, Andrés and Laine, Jesus (2012) 'Using artificial neural networks for open-loop tomography.', Optics express., 20 (3). pp. 2420-2434.


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.

Item Type:Article
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Publisher 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: Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.
Date accepted:No date available
Date deposited:29 November 2012
Date of first online publication:January 2012
Date first made open access:No date available

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