Curvature Wavefront Sensing for the Large Synoptic Survey Telescope

TL;DR

This paper presents advanced curvature wavefront sensing algorithms tailored for the LSST, addressing unique challenges like large obscuration and fast optics, with simulations confirming their effectiveness and adaptability to other wide-field telescopes.

Contribution

The paper introduces novel extensions to curvature wavefront sensing algorithms specifically designed for LSST's unique optical challenges, demonstrating their effectiveness through simulations.

Findings

Algorithms show convergence and linearity in simulations.

Negligible noise effects compared to atmospheric disturbances.

Extensions are adaptable to other wide-field optical systems.

Abstract

The Large Synoptic Survey Telescope (LSST) will use an active optics system (AOS) to maintain alignment and surface figure on its three large mirrors. Corrective actions fed to the LSST AOS are determined from information derived from 4 curvature wavefront sensors located at the corners of the focal plane. Each wavefront sensor is a split detector such that the halves are 1mm on either side of focus. In this paper we describe the extensions to published curvature wavefront sensing algorithms needed to address challenges presented by the LSST, namely the large central obscuration, the fast f/1.23 beam, off-axis pupil distortions, and vignetting at the sensor locations. We also describe corrections needed for the split sensors and the effects from the angular separation of different stars providing the intra- and extra-focal images. Lastly, we present simulations that demonstrate convergence, linearity, and negligible noise when compared to atmospheric effects when the algorithm extensions are applied to the LSST optical system. The algorithm extensions reported here are generic and can easily be adapted to other wide-field optical systems including similar telescopes with large central obscuration and off-axis curvature sensing.