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A Split LGS/NGS Atmospheric Tomography for MCAO and MOAO

Abstract 158

Submitted by Luc GILLES


L. Gilles, B. Ellerbroek


Thirty Meter Telescope


Minimum variance tomographic wavefront reconstruction is described in the litterature as a 2-step process consisting in the estimation using statistical priors of a small number of vertically distributed layers approximately representing the real atmosphere above the telescope, followed by least-squares fitting of this estimate onto the deformable mirrors (DMs) in the system. The fitting step depends solely on the geometric configuration of the DMs, which can be in series like in multi conjugate adaptive optics (MCAO), in parallel like in multi object adaptive optics (MOAO), or in a combination of both. The estimation problem is performed from wavefront sensor (WFS) measurements obtained from multiple guide stars. For many science cases, laser guide stars (LGSs) are required to increase sky coverage, but in this case a small number of three dimensional atmospheric low-order modes producing only piston and tip/tilt (PTT) in the LGS WFS pupils are unsensed due to the uncertain position of the LGSs on the sky. One possible approach to sense these null modes is to incorporate low-order natural guide star (NGS) WFS measurements in the estimation problem, although this complicates the practical implementation of the reconstruction algorithms. To overcome such complications, a split LGS/NGS wavefront control architecture is presented for MCAO and MOAO. Such an architecture implements two separate control loops driven independently by the LGS and NGS measurements, with distinct wavefront reconstruction algorithms applied to the LGS- and NGS-controlled modes. Control of the NGS modes is now a low dimensional problem, so that the reconstruction algorithm can be rapidly updated when the NGS asterism or the atmospheric conditions change. Sample Monte Carlo full-order simulation results illustrating the comparative performance of the combined and split approaches are discussed.

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