arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.10225astro-ph.IM
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各种观测条件下极端自适应光学系统预测的鲁棒性:使用VLT/SPHERE自适应光学数据的分析

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

M. A. M. van Kooten, Niek Doelman, Matthew Kenworthy

摘要:

对于高对比度成像(HCI)系统,如VLT/SPHERE,系统在小角度分离时的性能会受到科学图像中风驱动光晕的影响。该光晕是由于测量波前相位和应用相位校正之间的有限时间而导致的自适应光学(AO)系统中的伺服滞后误差的结果。减轻伺服滞后误差的一种方法是预测控制。我们的目标是估计和了解潜在的天空性能,线性数据驱动的预测将提供VLT/SPHERE在各种湍流条件下。我们使用了线性最小均方误差预测器,并将其应用到27个不同的AO遥测数据集从VLT/SPHERE采取了许多夜晚在各种湍流条件下。我们评估的预测器的性能,使用残余波前相位方差作为性能指标。我们表明,预测总是导致在减少的时间波前相位方差相比,目前的VLT/SPHERE AO性能。我们发现一个平均改善因子为5.1的相位方差预测相比,VLT/SPHERE残差。当与理想化的VLT/SPHERE相比时,我们发现改进因子为2.0。在我们的27个不同的情况下,我们发现预测结果在一个较小的传播的残留时间相位方差。最后,我们表明,有没有好处,包括空间信息的预测相反,可能已经从冻结流假设的预期。一个纯粹的时间预测是最适合的AO上VLT/SPHERE。

英文摘要:

For high-contrast imaging (HCI) 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. 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. 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. 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.

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