Poster + Paper
13 December 2020 Wind estimates from layer-oriented MCAO telemetry: working towards wavefront prediction
Author Affiliations +
Conference Poster
Abstract
Reliable wavefront prediction can improve adaptive optics (AO) performance, along with a proper control implementation. The accuracy of the estimated wind vector of the conjugated layers from AO telemetry is a crucial factor in the stability of the AO loop. For a layer-oriented MCAO system, the wind vector of the conjugated layer can be directly extracted from the corresponding wavefront sensor data. We investigate wind vector extraction using laboratory wavefront sensor data from LINC-NIRVANA (LN). The LN MCAO system senses and corrects for two atmospheric turbulent layers, the ground layer and a highaltitude layer (≈ 7km above the telescope pupil). For the ground layer, the stars’ footprints overlap completely. However, for the high layer, the footprints are spatially separated according to the asterism, and we must deal with a partially illuminated scenario. Reliable wavefront prediction, given the wind vector from AO telemetry, can virtually fill the non-illuminated sub-apertures in the direction of the wind, improving the performance of the high layer closed-loop. In this paper, we look into the efficacy of the wind velocity extraction using LN as the lab-setup for both fully and partially illuminated scenarios.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kalyan Kumar Radhakrishnan Santhakumari, Saavidra Perera, Florian Briegel, Thomas M. Herbst, Jörg-Uwe Pott, and Roberto Ragazzoni "Wind estimates from layer-oriented MCAO telemetry: working towards wavefront prediction", Proc. SPIE 11448, Adaptive Optics Systems VII, 1144862 (13 December 2020); https://doi.org/10.1117/12.2561368
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KEYWORDS
Wavefronts

Adaptive optics

Stars

Wavefront sensors

Atmospheric sensing

Control systems

Sensing systems

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