Recent work by Oberti+22 argued and showed that classical astronomical adaptive-optics tomography performance can be further improved by carefully designing and configuring the system to encompass and exploit any built-in super-resolution (SR) capabilities.
Our goal now is to further materialise the concept by outlining the key models to compute SR-enabling tomographic reconstructors for AO.
For that we assume the form of a review paper where we (i) clarify how model-and-deploy static reconstructors arise naturally from the solution of the inverse problem and how to make them cope with closed-loop systems, (ii) how this solution is obtained as a limiting-case of a properly-conceived optimal stochastic control problem, (iii) review the two forms of the minimum-mean-squared-error (MMSE) tomographic reconstructors, highlighting the necessary adaptations to accommodate super-resolution, (iii) review the implementation in either dense-format vector-matrix-multiplication or sparse iterative forms and (iv) discuss the implications for runtime and off-line real-time implementations.
We illustrate our examples with simulations/on-sky results when possible for 10m and 40m-scale systems.
The adaptive optics community is turning its attention to super-resolution-enabling designs (SR). These allow wavefront reconstruction at a higher-resolution from multiple lower-resolution samples. Such are the cases of tomographic sytems using multiple laser guide star Shack-Hartmann (SH) measurements.The extension of this concept to the pyramid wavefront sensors (PyWFS) has already been advocated, but the concept is not as straightforward as initially thought.
Our goal is to provide a general proof of how SR can be exploited with PyWFSs, where we can draw from the analogy with SH-WFSs and where it they fundamentally different. Furthermore we show how phase and amplitude aberrations can be measured concomitantly and the new applications this opens.
We provide analytic (diffraction theory), numerical (Monte Carlo physical optics simulations) and tentatively experimental (on-sky @ REVOLT) demonstrations.
We illustrate results with several achievable performance metrics (Strehl-ratio, WFE, etc), aliasing rejection and noise propagation as well as design guidelines for how to increase SR capabilities across different PyWFS configurations.
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