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Durham e-Theses
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Turbulence and wind velocity profiles from adaptive optics telemetry: a general and scalable solution for extremely large telescopes

LAIDLAW, DOUGLAS,JOHN (2020) Turbulence and wind velocity profiles from adaptive optics telemetry: a general and scalable solution for extremely large telescopes. Doctoral thesis, Durham University.

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Advanced Adaptive Optics (AO) instruments on ground-based telescopes require accurate knowledge of the strength and velocity of atmospheric turbulence. Measuring these parameters as a function of altitude assists point spread function reconstruction, AO temporal control techniques, smart scheduling of science cases and is required by wide-field AO systems to optimise the reconstruction of an observed wavefront.

The variability of the atmosphere makes it important to have a measure of the turbulence profile in real-time. This measurement can be performed by iteratively fitting an analytically generated covariance matrix to the cross-covariance of Shack–Hartmann Wavefront Sensor (SHWFS) centroids. In this study we explore the benefits of reducing the number of cross-covariance data points and fitting to a covariance map Region of Interest (ROI). Both of these methods are based on the SLOpe Detection And Ranging (SLODAR) technique. A technique for using the covariance map ROI to measure and compensate for SHWFS misalignments is also introduced. We compare the accuracy of covariance matrix and map ROI optical turbulence profiling using simulated data from CANARY, an AO demonstrator on the 4.2 m William Herschel Telescope (WHT), La Palma. It is shown that the covariance map ROI optimises the accuracy of turbulence profiling. In addition, we show that the covariance map ROI reduces the fitting time for an Extremely Large Telescope-scale (ELT-scale) system by a factor of 72.

SLODAR spatio-temporal analysis can be used to visualise the wind velocity profile. However, the limited altitude-resolution of current AO systems makes it difficult to disentangle the movement of independent layers. We address this issue and introduce a novel technique that uses SLODAR data analysis for automated wind velocity profiling. Simulated data from CANARY is used to demonstrate the proficiency of the technique.

We apply our turbulence and wind velocity profiling techniques on-sky using data from both CANARY and the Adaptive Optics Facility (AOF). The AOF is on the 8.2m Yepun telescope at the Very Large Telescope (VLT), Paranal. On-sky turbulence and wind velocity profiles from CANARY are compared to contemporaneous profiles from Stereo-SCIDAR, a dedicated high-resolution atmospheric profiler. Wind velocity profiles from CANARY and the AOF are compared to the European Centre for Medium-range Weather Forecasts (ECMWF). We also present AOF time sequences that show detailed examples of turbulence and wind velocity profiles at the VLT.

The software packages that we developed to collect all of the presented results are open-source. They can be configured to any tomographic AO system.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Faculty and Department:Faculty of Science > Physics, Department of
Thesis Date:2020
Copyright:Copyright of this thesis is held by the author
Deposited On:06 Feb 2020 14:30

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