Impact of time-variant turbulence behavior on prediction for adaptive optics systems

Abstract

For high contrast imaging systems, the time delay is one of the major limiting factors for the performance of the extreme adaptive optics (AO) sub-system and, in turn, the final contrast. The time delay is due to the finite time needed to measure the incoming disturbance and then apply the correction. By predicting the behavior of the atmospheric disturbance over the time delay we can in principle achieve a better AO performance. Atmospheric turbulence parameters which determine the wavefront phase fluctuations have time-varying behavior. We present a stochastic model for wind speed and model time-variant atmospheric turbulence effects using varying wind speed. We test a low-order, data-driven predictor, the linear minimum mean square error predictor, for a near-infrared AO system under varying conditions. Our results show varying wind can have a significant impact on the performance of wavefront prediction, preventing it from reaching optimal performance. The impact depends on the strength of the wind fluctuations with the greatest loss in expected performance being for high wind speeds.

10 pages, 8 figures; Accepted to JOSA A March 2019

References in corpus (4)

• The Gemini Planet Imager: First Light
• On-sky performance analysis of the vector Apodizing Phase Plate coronagraph on MagAO/Clio2
• Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment
• Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results

Cited by in corpus (2)

• Wavefront prediction using artificial neural networks for open-loop Adaptive Optics
• Status of predictive wavefront control on Keck II adaptive optics bench: on-sky coronagraphic results