13 September 2012 Tomographic reconstructor for multi-object adaptive optics using artificial neural networks
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Abstract
Multi-object adaptive optics requires a tomographic reconstructor to compute the AO correction for scientific targets within the field, using measurements of incoming turbulence from guide stars angularly separated from the science targets. We have developed a reconstructor using an artificial neural network, which is trained in simulation only. We obtained similar or better results than current reconstructors, such as least-squares and Learn and Apply, in simulation and also tested the new technique in the laboratory. The method is robust and can cope well with variations in the atmospheric conditions. We present the technique, our latest results and plans for a full MOAO experiment.
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Dani Guzman, Dani Guzman, Alexandre T. Mello, Alexandre T. Mello, James Osborn, James Osborn, Francisco J. De Cos, Francisco J. De Cos, Marlon Gómez, Marlon Gómez, Timothy Butterley, Timothy Butterley, Nicole David, Nicole David, Nieves Roqueñi, Nieves Roqueñi, Richard M. Myers, Richard M. Myers, Andrés Guesalaga, Andrés Guesalaga, Matias Salas, Matias Salas, } "Tomographic reconstructor for multi-object adaptive optics using artificial neural networks", Proc. SPIE 8447, Adaptive Optics Systems III, 844740 (13 September 2012); doi: 10.1117/12.925355; https://doi.org/10.1117/12.925355
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