Real-time Adaptive Optics with pyramid wavefront sensors: Accurate wavefront reconstruction using iterative methods

TL;DR

This paper demonstrates that iterative algorithms like conjugate gradient and steepest descent can efficiently and accurately reconstruct wavefronts in real-time adaptive optics systems using pyramid sensors, reducing computational costs.

Contribution

The study introduces and compares iterative reconstruction algorithms for pyramid wavefront sensors, showing they are computationally efficient and comparable in quality to traditional methods.

Findings

Iterative algorithms achieve high-quality wavefront reconstruction.

These methods significantly reduce computational effort.

Performance is validated through end-to-end simulations.

Abstract

In this paper, we address the inverse problem of fast, stable, and high-quality wavefront reconstruction from pyramid wavefront sensor data for Adaptive Optics systems on Extremely Large Telescopes. For solving the indicated problem we apply well-known iterative mathematical algorithms, namely conjugate gradient, steepest descent, Landweber, Landweber-Kaczmarz and steepest descent-Kaczmarz iteration based on theoretical studies of the pyramid wavefront sensor. We compare the performance (in terms of correction quality and speed) of these algorithms in end-to-end numerical simulations of a closed adaptive loop. The comparison is performed in the context of a high-order SCAO system for METIS, one of the first-light instruments currently under design for the Extremely Large Telescope. We show that, though being iterative, the analyzed algorithms, when applied in the studied context, can be implemented in a very efficient manner, which reduces the related computational effort significantly. We demonstrate that the suggested analytically developed approaches involving iterative algorithms provide comparable quality to standard matrix-vector-multiplication methods while being computationally cheaper.