Wind Estimates From Layer-Oriented MCAO Telemetry: Working Towards Wavefront Prediction

摘要

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 high-altitude layer (≈ 7km above the telescope pupil). For the ground layer, the stars' footprints overlap completely. However, for the high layer, the footprints arc spatially separated according to the astcrism, 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.