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Tomography reconstruction using the Learn and Apply (L&A) algorithm: Simulations and tests results at the SESAME bench.

Abstract 122

Submitted by Fabrice VIDAL


F. Vidal, E. Gendron, M. Brangier, A. Sevin, G. Rousset, Z. Hubert




In the framework of the MOAO demonstrator CANARY, we developped a new concept of tomography algorithm that allows to measure the tomographic reconstructor directly on-sky, using or not, a priori from the turbulence profile. This simple algorithm, working in open-loop, uses the measured covariance of slopes between all the wavefront sensors (WFS) to deduce the geometric and atmospheric parameters that are used to compute the tomographic reconstructor. We have also developped a method that measures and takes into account all the misalignments between the WFS in order to calibrate any MOAO instrument.

We present the simulations and expected performance of the CANARY instrument with this algorithm and the last results performed at the SESAME bench.

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