Robustness of prediction for extreme adaptive optics systems under various observing conditions: An analysis using VLT/SPHERE adaptive optics data

Robustness of prediction for extreme adaptive optics systems under various observing conditions: An analysis using VLT/SPHERE adaptive optics data

van Kooten, M.A.M.
Doelman, N.J.
Kenworthy, M.

2020

For high-contrast imaging systems, such as VLT/SPHERE, the performance of the system at small angular separations is contaminated by the wind-driven halo in the science image. This halo is a result of the servo-lag error in the adaptive optics (AO) system due to the finite time between measuring the wavefront phase and applying the phase correction. One approach to mitigating the servo-lag error is predictive control. Aims. We aim to estimate and understand the potential on-sky performance that linear data-driven prediction would provide for VLT/SPHERE under various turbulence conditions. Methods. We used a linear minimum mean square error predictor and applied it to 27 different AO telemetry data sets from VLT/SPHERE taken over many nights under various turbulence conditions. We evaluated the performance of the predictor using residual wavefront phase variance as a performance metric. Results. We show that prediction always results in a reduction in the temporal wavefront phase variance compared to the current VLT/SPHERE AO performance. We find an average improvement factor of 5.1 in phase variance for prediction compared to the VLT/SPHERE residuals. When comparing to an idealised VLT/SPHERE, we find an improvement factor of 2.0. Under our 27 different cases, we find the predictor results in a smaller spread of the residual temporal phase variance. Finally, we show there is no benefit to including spatial information in the predictor in contrast to what might have been expected from the frozen flow hypothesis. A purely temporal predictor is best suited for AO on VLT/SPHERE. Conclusions. Linear prediction leads to a significant reduction in phase variance for VLT/SPHERE under a variety of observing conditions and reduces the servo-lag error. Furthermore, prediction improves the reliability of the AO system performance, making it less sensitive to different conditions.

Forecasting
Mean square error
Signal receivers
Turbomachinery
Wavefronts
Adaptive optics systems
Extreme adaptive optics
Frozen flow hypothesis
High contrast imaging
Linear minimum mean square errors
Performance metrices
Spatial informations
Turbulence conditions

http://resolver.tudelft.nl/uuid:402d7b4c-9bdb-4dd3-9ef7-82aefb701081

https://doi.org/10.1051/0004-6361/201937076

876018

EDP Sciences

0004-6361

Astronomy and Astrophysics, 636

201937076

article