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Open loop tomography with artificial neural networks on CANARY : on-sky results.

Osborn, J. and Guzman, D. and De Cos Juez, F.J. and Basden, A.G. and Morris, T.J. and Gendron, E. and Butterley, T. and Myers, R.M. and Guesalaga, A. and Sanchez Lasheras, F. and Gomez Victoria, M. and Sánchez Rodríguez, M.L. and Gratadour, D. and Rousset, G. (2014) 'Open loop tomography with artificial neural networks on CANARY : on-sky results.', Monthly notices of the Royal Astronomical Society., 441 (3). pp. 2508-2514.


We present recent results from the initial testing of an artificial neural network (ANN)-based tomographic reconstructor Complex Atmospheric Reconstructor based on Machine lEarNing (CARMEN) on CANARY, an adaptive optics demonstrator operated on the 4.2 m William Herschel Telescope, La Palma. The reconstructor was compared with contemporaneous data using the Learn and Apply (L&A) tomographic reconstructor. We find that the fully optimized L&A tomographic reconstructor outperforms CARMEN by approximately 5 per cent in Strehl ratio or 15 nm rms in wavefront error. We also present results for CANARY in Ground Layer Adaptive Optics mode to show that the reconstructors are tomographic. The results are comparable and this small deficit is attributed to limitations in the training data used to build the ANN. Laboratory bench tests show that the ANN can outperform L&A under certain conditions, e.g. if the higher layer of a model two layer atmosphere was to change in altitude by ∼300 m (equivalent to a shift of approximately one tenth of a subaperture).

Item Type:Article
Keywords:Atmospheric effects, Instrumentation: adaptive optics.
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Publisher statement:This article has been accepted for publication in Monthly notices of the Royal Astronomical Society. ©: 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Date accepted:12 April 2014
Date deposited:28 April 2015
Date of first online publication:20 May 2014
Date first made open access:No date available

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