Non-linear wavefront reconstruction methods for pyramid sensors using Landweber and Landweber-Kaczmarz iteration

TL;DR

This paper introduces two non-linear iterative algorithms, Landweber and Landweber-Kaczmarz, for wavefront reconstruction in pyramid sensors, improving accuracy especially for non-modulated sensors in adaptive optics systems.

Contribution

The paper presents novel non-linear Landweber and Landweber-Kaczmarz methods tailored for pyramid wavefront sensors, addressing limitations of linear models in realistic scenarios.

Findings

High-quality wavefront estimation achieved

Effective for non-modulated sensors

Maintains feasible computational effort

Abstract

Accurate and robust wavefront reconstruction methods for pyramid wavefront sensors are in high demand as these sensors are planned to be part of many instruments currently under development for ground based telescopes. The pyramid sensor relates the incoming wavefront and its measurements in a non-linear way. Nevertheless, almost all existing reconstruction algorithms are based on a linearization of the model. The assumption of a linear pyramid sensor response is justifiable in closed loop AO when the measured phase information is small but may not be reasonable in reality due to unpreventable errors depending on the system such as non common path aberrations. In order to solve the non-linear inverse problem of wavefront reconstruction from pyramid sensor data we introduce two new methods based on the non-linear Landweber and Landweber-Kaczmarz iteration. Using these algorithms we experience high-quality wavefront estimation especially for the non-modulated sensor by still keeping the numerical effort feasible for large-scale AO systems.