No abstract

Canary is the Multi-Object Adaptive Optics (MOAO) pathfinder for the future MOAO-assisted Integral-Field Units (IFU) proposed for Extremely Large Telescopes (ELT). The MOAO concept relies on tomographically reconstructing the turbulence using multiple measurements along different lines of sight.Tomography requires the knowledge of the statistical turbulence parameters, commonly recovered from the system telemetry using a dedicated profiling technique. For demonstration purposes with the MOAO pathfinder Canary , this identification is performed thanks to the Learn & Apply (L&A) algorithm, that consists in modelfitting the covariance matrix of WFS measurements dependant on relevant parameters: C 2 n (h) profile, outer scale profile and system mis-registration.We explore an upgrade of this algorithm, the Learn 3 Steps (L3S) approach, that allows one to dissociate the identification of the altitude layers from the ground in order to mitigate the lack of convergence of the required empirical covariance matrices therefore reducing the required length of data time-series for reaching a given accuracy. For nominal observation conditions, the L3S can reach the same level of tomographic error in using five times less data frames than the L&A approach.The L3S technique has been applied over a large amount of Canary data to characterize the turbulence above the William Herschel Telescope (WHT). These data have been acquired the 13th, 15th, 16th, 17th and 18th September 2013 and we find 0.67"/8.9m/3.07m.s −1 of total seeing/outer scale/wind-speed, with 0.552"/9.2m/2.89m.s −1 below 1.5 km and 0.263"/10.3m/5.22m.s −1 between 1.5 and 20 km. We have also determined the high altitude layers above 20 km, missed by the tomographic reconstruction on Canary , have a median seeing of 0.187" and have occurred 16% of observation time.

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 2 N 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 Shark-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 and large C 2 N fraction of the ground layer of 54.3 %. The C 2 N profile has a good agreement with the result from literatures, except for the ground layer. The median value of the outer scale is 25.5 m and the outer scale is larger at higher altitudes, and these trends of the outer scale are consistent with findings in literatures.