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For high-contrast imaging (HCI) systems, such as VLT/SPHERE, the performanceof the system at small angular separations is contaminated by the wind-drivenhalo in the science image. This halo is a result of the servo-lag error in theadaptive optics (AO) system due to the finite time between measuring thewavefront phase and applying the phase correction. One approach to mitigatingthe servo-lag error is predictive control. We aim to estimate and understandthe potential on-sky performance that linear data-driven prediction wouldprovide for VLT/SPHERE under various turbulence conditions. We used a linearminimum mean square error predictor and applied it to 27 different AO telemetrydata sets from VLT/SPHERE taken over many nights under various turbulenceconditions. We evaluated the performance of the predictor using residualwavefront phase variance as a performance metric. We show that predictionalways results in a reduction in the temporal wavefront phase variance comparedto the current VLT/SPHERE AO performance. We find an average improvement factorof 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 of2.0. Under our 27 different cases, we find the predictor results in a smallerspread of the residual temporal phase variance. Finally, we show there is nobenefit to including spatial information in the predictor in contrast to whatmight have been expected from the frozen flow hypothesis. A purely temporalpredictor is best suited for AO on VLT/SPHERE.

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