The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing

TL;DR

This paper explores the use of spatiotemporal Gaussian process models for predictive control in adaptive optics, demonstrating significant wavefront error reduction when atmospheric conditions are well known and analyzing data utility for optimal prediction.

Contribution

It introduces a Gaussian process-based predictive reconstructor for adaptive optics that is optimal in least-squares sense and evaluates the impact of knowledge and data selection on prediction accuracy.

Findings

Perfect knowledge of atmospheric evolution reduces residual wavefront variance by up to 3.5 times.

Using only effective wind speed reduces variance by a factor of 2.3.

Optimal selection of past data frames improves prediction accuracy by 10-15%.

Abstract

Time-delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time-delay error, providing significant performance gains, especially for high-contrast imaging. However, the predictive controller's performance depends on factors such as the WFS type, the measurement noise, the AO system's geometry, and the atmospheric conditions. This work studies the limits of prediction under different imaging conditions through spatiotemporal Gaussian process models. The method provides a predictive reconstructor that is optimal in the least-squares sense, conditioned on the fixed times series of WFS data and our knowledge of the atmosphere. We demonstrate that knowledge is power in predictive AO control. With an SHS-based extreme AO instrument, perfect knowledge of Frozen Flow evolution (wind and Cn2 profile) leads to a reduction of the residual wavefront phase variance up to a factor of 3.5 compared to a non-predictive approach. If there is uncertainty in the profile or evolution models, the gain is more modest. Still, assuming that only effective wind speed is available (without direction) led to reductions in variance by a factor of 2.3. We also study the value of data for predictive filters by computing the experimental utility for different scenarios to answer questions such as: How many past data frames should the prediction filter consider, and is it always most advantageous to use the most recent data? We show that within the scenarios considered, more data consistently increases prediction accuracy. Further, we demonstrate that given a computational limitation on how many past frames we can use, an optimized selection of $n$ past frames leads to a 10-15% additional improvement in RMS over using the n latest consecutive frames of data.