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Fresnel propagation of starlight after it passes through high altitude turbulence in the Earth's atmosphere results in random fluctuations of the intensity at ground level, known as scintillation. This effect adds random noise to photometric measurements with ground-based optical telescopes. Spatial correlation of the intensity fluctuations means that the fractional photometric noise due to scintillation may be substantially smaller for a sparse array of small aperture telescopes than for a single large aperture of the same total area. Assuming that the photometric noise for each telescope is independent, averaging the light curves measured by N telescopes reduces the noise by a factor of |$\sqrt{N}$|⁠. For example, for bright stars, the signal-to-noise ratio of a 2.54 m telescope can be achieved for an array of thirty 20 cm telescopes if the scintillation noise measured for each telescope is uncorrelated. In this paper, we present results from simulation and from observations at the Isaac Newton Telescope. These explore the impact that several parameters have on the measured correlation of the scintillation noise between neighbouring telescopes. We show that there is significant correlation between neighbouring telescopes with separations parallel to the wind direction of the dominant high altitude turbulent layer. We find that the telescopes in an array should be separated by at least twice their aperture diameter so that there is negligible correlation of the photometric noise. We discuss additional benefits of using sparse telescope arrays, including reduced cost and increased field of view. [ABSTRACT FROM AUTHOR]

Ground-based adaptive optics (AO) systems can use temporal control techniques to greatly improve image resolution. A measure of wind velocity as a function of altitude is needed to minimize the temporal errors associated with these systems. Spatio-temporal analysis of AO telemetry can express the wind velocity profile using the slope detection and ranging (SLODAR) technique. However, the limited altitude-resolution of current AO systems makes it difficult to disentangle the movement of independent layers. It is therefore a challenge to create an algorithm that can recover the wind velocity profile through SLODAR data analysis. In this study, we introduce a novel technique for automated wind velocity profiling from AO telemetry. Simulated and on-sky centroid data from CANARY – an AO testbed on the 4.2 m William Herschel telescope, La Palma – is used to demonstrate the proficiency of the technique. Wind velocity profiles measured on-sky are compared to contemporaneous measurements from Stereo-SCIDAR, a dedicated high-resolution atmospheric profiler. They are also compared to European centre for medium-range weather forecasts. The software package that we developed to complete this study is open source. [ABSTRACT FROM AUTHOR]

Advanced adaptive optics (AO) instruments on ground-based telescopes require accurate knowledge of the atmospheric turbulence strength as a function of altitude. This information assists point spread function reconstruction, AO temporal control techniques and is required by wide-field AO systems to optimize the reconstruction of an observed wavefront. The variability of the atmosphere makes it important to have a measure of the optical turbulence profile in real time. This measurement can be performed by 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 cross-covariance data points to a covariance map region of interest (ROI). 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 both simulated and on-sky data from CANARY, an AO demonstrator on the 4.2 m William Herschel telescope, La Palma. On-sky CANARY results are compared to contemporaneous profiles from Stereo-SCIDAR  – a dedicated high-resolution optical turbulence profiler. It is shown that the covariance map ROI optimizes the accuracy of AO telemetry optical turbulence profiling. In addition, we show that the covariance map ROI reduces the fitting time for an extremely large telescope-scale system by a factor of 72. The software package we developed to collect all of the presented results is now open source. [ABSTRACT FROM AUTHOR]