We present FitAO, which is an open-source Python-based concept platform for algorithmic development in adaptive optics (AO). Control and reconstruction algorithms designed on FitAO can be executed simultaneously on multiple supported end-to-end simulation environments. It utilizes interface specifications of OpenAI Gym library enabling direct access to an extensive set of control algorithms. With these properties, FitAO aims to facilitate comparative studies of AO control and reconstruction algorithms, and pave the way for modern data-driven and hybrid algorithms. We provide a brief tutorial example and discuss future development.
SLODAR (SLOpe Detection And Ranging) methods recover the atmospheric turbulence profile from cross-correlations of wavefront sensor (WFS) measurements, based on known turbulence models. Our work grows out of several experiments showing that turbulence statistics can deviate significantly from the classical Kolmogorov/ von Kármán models, especially close to the ground. We present a novel SLODAR-type method which simultaneously recovers both the turbulence profile in the atmosphere and the turbulence statistics at the ground layer - namely the slope of the spatial frequency power law. We consider its application to outer scale (L0)- reconstruction and investigate the limits of the joint estimation of such parameters.
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