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<p>Recent work by Oberti et al, (<i>Astron. Astrophys.</i>, <i>667</i>, 48, 2022) argued and made a compelling case that classical astronomical adaptive optics (AO) tomography performance can be further enhanced by carefully designing and optically configuring the system to leverage inherent super-resolution (SR) capabilities. Our goal here is to further materialise the concept by providing the means to compute SR-enabling tomographic reconstructors for AO and showcase its broad uptake on soon every 10&#xa0;m-class VIS/NIR telescopes and Giant Segmented Mirror Telescopes of up to 40&#xa0;m in diameter. To that end we indicate the necessary tomography generalisations where we: <i>(i)</i> clarify how <i>model-and-deploy</i> is a generic methodological umbrella for linear minimum-mean-squared-error (LMMSE) tomographic reconstructors arising naturally from the solution of the tomographic inverse problem, thus unifying various solutions presented as distinct in the literature within a single framework, <i>(ii)</i> recall how such solutions are found as limiting cases of a model-based optimal control problem, thus elucidating how <i>pseudo-open-loop control</i> is a feature of the latter that allows LMMSE reconstructors to be adapted to closed-loop systems, <i>(iii)</i> review the two forms of the LMMSE tomographic reconstructors, highlighting the necessary adaptations to accommodate super-resolution, <i>(iv)</i> review the implementation in either dense-format vector-matrix-multiplication or sparse iterative forms and <i>(v)</i> discuss the implications for runtime and off-line real-time implementations, anticipating widespread adoption. We illustrate our examples with physical-optics numerical simulations for 10&#xa0;m and 40&#xa0;m-scale systems showing the performance benefits of super-resolution in the order of several tens of nm rms and the computational burden associated.</p>

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Super-resolution-enabled atmospheric tomography for astronomical multi-wavefront-sensor adaptive-optics systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Carlos M. Correia,
  • Pierre Jouve,
  • Jesse Cranney,
  • Guido Agapito,
  • Cédric Taïssir Heritier

摘要

Recent work by Oberti et al, (Astron. Astrophys., 667, 48, 2022) argued and made a compelling case that classical astronomical adaptive optics (AO) tomography performance can be further enhanced by carefully designing and optically configuring the system to leverage inherent super-resolution (SR) capabilities. Our goal here is to further materialise the concept by providing the means to compute SR-enabling tomographic reconstructors for AO and showcase its broad uptake on soon every 10 m-class VIS/NIR telescopes and Giant Segmented Mirror Telescopes of up to 40 m in diameter. To that end we indicate the necessary tomography generalisations where we: (i) clarify how model-and-deploy is a generic methodological umbrella for linear minimum-mean-squared-error (LMMSE) tomographic reconstructors arising naturally from the solution of the tomographic inverse problem, thus unifying various solutions presented as distinct in the literature within a single framework, (ii) recall how such solutions are found as limiting cases of a model-based optimal control problem, thus elucidating how pseudo-open-loop control is a feature of the latter that allows LMMSE reconstructors to be adapted to closed-loop systems, (iii) review the two forms of the LMMSE tomographic reconstructors, highlighting the necessary adaptations to accommodate super-resolution, (iv) review the implementation in either dense-format vector-matrix-multiplication or sparse iterative forms and (v) discuss the implications for runtime and off-line real-time implementations, anticipating widespread adoption. We illustrate our examples with physical-optics numerical simulations for 10 m and 40 m-scale systems showing the performance benefits of super-resolution in the order of several tens of nm rms and the computational burden associated.