Spatio-angular Minimum-variance Tomographic Controller for Multi-Object Adaptive Optics systems

Multi-object astronomical adaptive-optics (MOAO) is now a mature wide-field observation mode to enlarge the adaptive-optics-corrected field in a few specific locations over tens of arc-minutes. The work-scope provided by open-loop tomography and pupil conjugation is amenable to a spatio-angular Linear-Quadratic Gaussian (SA-LQG) formulation aiming to provide enhanced correction across the field with improved performance over static reconstruction methods and less stringent computational complexity scaling laws. Starting from our previous work [1], we use stochastic time-progression models coupled to approximate sparse measurement operators to outline a suitable SA-LQG formulation capable of delivering near optimal correction. Under the spatio-angular framework the wave-fronts are never explicitly estimated in the volume,providing considerable computational savings on 10m-class telescopes and beyond. We find that for Raven, a 10m-class MOAO system with two science channels, the SA-LQG improves the limiting magnitude by two stellar magnitudes when both Strehl-ratio and Ensquared-energy are used as figures of merit. The sky-coverage is therefore improved by a factor of 5.

Anti-aliasing Wiener filtering for wave-front reconstruction in the spatial-frequency domain for high-order astronomical adaptive-optics systems

Computationally-efficient wave-front reconstruction techniques for astronomical adaptive optics systems have seen a great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered large attention specially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl-ratio) and further develop formulae for the anti-aliasing Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e. discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performance results as a function of an ensemble of for ward models for the Shack-Hartmann wave-front sensor (using sparse and non-sparse representations) and compute point-spread function raw intensities. We find that for a 32x32 single-conjugated adaptive optics system the aliasing propagation coefficient is roughly 60% of the least-squares filters whereas the noise propagation is around 80%. Contrast improvements of factors of up to 2 are achievable across the field in H-band. For current and next generation high-contrast imagers, despite better aliasing mitigation, anti-aliasing Wiener filtering cannot be used as a stand-alone method and must therefore be used in combination with optical spatial filters deployed before image formation takes actual place.