Key wavefront sensors features for laser-assisted tomographic adaptive optics systems on the Extremely Large Telescope

TL;DR

This paper analyzes key features of wavefront sensors for laser-assisted tomographic adaptive optics on the Extremely Large Telescope, focusing on design trade-offs and the potential of super resolution to optimize performance.

Contribution

It introduces a sensitivity analysis of wavefront sensor parameters and proposes super resolution as a novel approach to improve ELT adaptive optics system design.

Findings

Super resolution can reduce pupil sampling requirements.

Trade-offs between sub-aperture size, field-of-view, and pixel sampling are critical.

Design insights for optimizing wavefront sensors at ELT scales.

Abstract

Laser guide star (LGS) wave-front sensing (LGSWFS) is a key element of tomographic adaptive optics system. However, when considering Extremely Large Telescope (ELT) scales, the LGS spot elongation becomes so large that it challenges the standard recipes to design LGSWFS. For classical Shack-Hartmann wave-front sensor (SHWFS), which is the current baseline for all ELT LGS-assisted instruments, a trade-off between the pupil spatial sampling [number of sub-apertures (SAs)], the SA field-of-view (FoV) and the pixel sampling within each SA is required. For ELT scales, this trade-off is also driven by strong technical constraints, especially concerning the available detectors and in particular their number of pixels. For SHWFS, a larger field of view per SA allows mitigating the LGS spot truncation, which represents a severe loss of performance due to measurement biases. For a given number of available detectors pixels, the SA FoV is competing with the proper sampling of the LGS spots, and/or the total number of SAs. We proposed a sensitivity analysis, and we explore how these parameters impacts the final performance. In particular, we introduce the concept of super resolution, which allows one to reduce the pupil sampling per WFS and opens an opportunity to propose potential LGSWFS designs providing the best performance for ELT scales.