William Herschel Telescope site characterization using the MOAO pathfinder CANARY on-sky data
O.A. Martin, C. M. Correia, E. Gendron, G. Rousset, F., Vidal, T.J. Morris, A.G. Basden, R.M. Myers, Y.H. Ono, B., Neichel, and T. Fusco

TL;DR
This study introduces an improved turbulence profiling algorithm for adaptive optics, applied to Canary data, enabling efficient characterization of atmospheric conditions above the William Herschel Telescope with less data.
Contribution
The paper presents the Learn 3 Steps (L3S) algorithm, enhancing turbulence profile identification by reducing data requirements compared to previous methods.
Findings
L3S achieves same accuracy with five times less data.
Characterized turbulence parameters above WHT, including seeing, outer scale, and wind speed.
Identified high-altitude layers with median seeing of 0.187" occurring 16% of the time.
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 model- fitting the covariance matrix of WFS measurements dependent on relevant parameters: 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…
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Taxonomy
TopicsAdaptive optics and wavefront sensing · Stellar, planetary, and galactic studies · Astronomy and Astrophysical Research
