Dynamic Tomographic Algorithms for Multi-Object Adaptive Optics: Increasing sky-coverage by increasing the limiting magnitude for Raven, a science and technology demonstrator
Date
2014-08-29
Authors
Jackson, Kate
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Abstract
This dissertation outlines the development of static and dynamic tomographic
wave-front (WF) reconstructors tailored to Multi-Object Adaptive Optics (MOAO).
They are applied to Raven, the first MOAO science and technology demonstrator
recently installed on an 8m telescope, with the goal of increasing the limiting magnitude
in order to increase sky coverage. The results of a new minimum mean-square
error (MMSE) solution based on spatio-angular (SA) correlation functions are shown,
which adopts a zonal representation of the wave-front and its associated signals. This
solution is outlined for the static reconstructor and then extended for the use of standalone
temporal prediction. Furthermore, it is implemented as the prediction model
in a pupil plane based Linear Quadratic Gaussian (LQG) algorithm. The algorithms
have been fully tested in the laboratory and compared to the results from Monte-
Carlo simulations of the Raven system. The simulations indicate that an increase
in limiting magnitude of up to one magnitude can be expected when prediction is implemented. Two or more magnitudes of improvement may be achievable when the
LQG is used. These results are confirmed by laboratory measurements.
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Keywords
Adaptive Optics, tomography, MOAO