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Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems

Title
Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems
Type
Article in International Scientific Journal
Year
2014
Authors
Correia, C
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Jackson, K
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Veran, JP
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Andersen, D
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Lardiere, O
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Bradley, C
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Authenticus ID: P-008-QK9
Abstract (EN): Multi-object adaptive optics (MOAO) systems are still in their infancy: their complex optical designs for tomographic, wide-field wavefront sensing, coupled with open-loop (OL) correction, make their calibration a challenge. The correction of a discrete number of specific directions in the field allows for streamlined application of a general class of spatio-angular algorithms, initially proposed in Whiteley et al. [J. Opt. Soc. Am. A 15, 2097 (1998)], which is compatible with partial on-line calibration. The recent Learn & Apply algorithm from Vidal et al. [ J. Opt. Soc. Am. A 27, A253 (2010)] can then be reinterpreted in a broader framework of tomographic algorithms and is shown to be a special case that exploits the particulars of OL and aperture-plane phase conjugation. An extension to embed a temporal prediction step to tackle sky-coverage limitations is discussed. The trade-off between lengthening the camera integration period, therefore increasing system lag error, and the resulting improvement in SNR can be shifted to higher guide-star magnitudes by introducing temporal prediction. The derivation of the optimal predictor and a comparison to suboptimal autoregressive models is provided using temporal structure functions. It is shown using end-to-end simulations of Raven, the MOAO science, and technology demonstrator for the 8 m Subaru telescope that prediction allows by itself the use of 1-magnitude-fainter guide stars. (C) 2013 Optical Society of America
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 13
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