Wavefront Phase Retrieval with Non-linear Curvature Sensors

TL;DR

This paper develops and compares algorithms for wavefront phase retrieval using non-linear curvature sensors, demonstrating that Gaussian preprocessing improves accuracy at low light levels, enabling better adaptive optics correction with faint stars.

Contribution

It introduces new algorithms for phase retrieval from four-plane curvature sensors and shows that Gaussian preprocessing significantly reduces photon requirements at low light levels.

Findings

Gaussian convolution reduces photon flux needed for accurate phase retrieval

Algorithms improve wavefront correction with faint reference stars

Simulation results validate the effectiveness of proposed methods

Abstract

Increasing interest in astronomical applications of non-linear curvature wavefront sensors for turbulence detection and correction makes it important to understand how best to handle the data they produce, particularly at low light levels. Algorithms for wavefront phase-retrieval from a four-plane curvature wavefront sensor are developed and compared, with a view to their use for low order phase compensation in instruments combining adaptive optics and Lucky Imaging. The convergence speed and quality of iterative algorithms is compared to their step-size and techniques for phase retrieval at low photon counts are explored. Computer simulations show that at low light levels, preprocessing by convolution of the measured signal with a gaussian function can reduce by an order of magnitude the photon flux required for accurate phase retrieval of low-order errors. This facilitates wavefront correction on large telescopes with very faint reference stars.