An efficient Reconstruction Method for Ground Layer Adaptive Optics with mixed Natural and Laser Guide Stars Powered by TCPDF (www.tcpdf.org) An efficient Reconstruction Method for Ground Layer Adaptive Optics with mixed Natural and Laser Guide Stars

The imaging quality of modern ground based telescopes like the planned European Extremely Large Telescope is affected by atmospheric turbu-lence. In consequence, they heavily depend on stable and high-performance Adaptive Optics (AO) systems. Using measurements of incoming light from guide stars, an AO system compensates for the effects of turbulence by adjusting so-called deformable mirror(s) (DM) in real-time. In this paper, we introduce a novel reconstruction method for the Ground Layer Adaptive Optics (GLAO). In the literature, a common ap-proach to this problem is to use Bayesian inference in order to model the specific noise structure appearing due to spot elongation (see [5]). This approach leads to large coupled systems with high computational effort. Recently, fast solvers of linear order, i.e., with computational complex-ity O(n), where n is the number of DM-actuators, have emerged [18]. However, the quality of such methods typically degrades in low flux con-ditions. Our key contribution is to achieve the high quality of the standard Bayesian approach while at the same time maintaining the linear order speed of the recent solvers. Our method is based on performing a separate preprocessing step before applying the cumulative reconstructor (CuReD, [27]). The efficiency and performance of the new reconstructor are demon-strated using the OCTOPUS, the official end-to-end simulation enviro-ment of the ESO for extremely large telescopes [15]. For more specific simulations we also use the MOST toolbox [1].