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Prior statistical knowledge of the atmospheric turbulence is essential for designing, optimizing and evaluating tomographic adaptive optics systems. We present the statistics of the vertical profiles of $C_N^2$ and the outer scale at Maunakea estimated using a Slope Detection And Ranging (SLODAR) method from on-sky telemetry taken by RAVEN, which is a MOAO demonstrator in the Subaru telescope. In our SLODAR method, the profiles are estimated by a fit of the theoretical auto- and cross-correlation of measurements from multiple Shack-Haltmann wavefront sensors to the observed correlations via the non-linear Levenberg-Marquardt Algorithm (LMA), and the analytic derivatives of the spatial phase structure function with respect to its parameters for the LMA are also developed. The estimated profile has the median total seeing of 0.460$^{primeprime}$ and large $C_N^2$ fraction of the ground layer of 54.3%. The $C_N^2$ profile has a good agreement with the result from literatures, except for the ground layer. The median value of the outer scale is 25.5m and the outer scale is larger at higher altitudes, and these trends of the outer scale are consistent with findings in literatures.
In this paper we report on the laboratory experiment we settled in the Shanghai Astronomical Observatory (SHAO) to investigate the pyramid wavefront sensor (WFS) ability to measure the differential piston on a sparse aperture. The ultimate goal is to
We present long term site testing statistics obtained at Dome C, Antarctica with various experiments deployed within the Astroconcordia programme since 2003. We give values of integrated turbulence parameters in the visible at ground level and above
We present laboratory experiments of surface wave turbulence excited by paddles in the deep water regime. The free surface is seeded with buoyant particles that are advected and dispersed by the flow. Positions and velocities of the floaters are meas
We present TurbuStat (v1.0): a Python package for computing turbulence statistics in spectral-line data cubes. TurbuStat includes implementations of fourteen methods for recovering turbulent properties from observational data. Additional features of
Gravitational wave observatories have always been affected by tele-seismic earthquakes leading to a decrease in duty cycle and coincident observation time. In this analysis, we leverage the power of machine learning algorithms and archival seismic da