Ground-layer adaptive-optics system modelling for the ChineseLarge Optical/Infrared Telescope

Peng, J.; Basden, A.; Osborn, J.

Abstract

The Chinese Large Optical/Infrared Telescope is currently undergoing design studies. A ground-layer adaptive-optics system (GLAO) has been proposed, with a 14 -arcmin field of view (FOV) and moderate adaptive-optics correction using a deformable secondary mirror, several laser guide stars (LGS) and wavefront sensors. Due to its large FOV and relatively complex structure, some important problems should be analysed to aid budget considerations for the whole telescope. In this paper, we propose a FOV-related evaluation factor to describe the GLAO performance and use an end-to-end Monte Carlo simulation to test its performance under different configurations. To accelerate the parameter space exploration speed, we split the parameter space of the GLAO system and apply a bottom-up search procedure for parameter selection. We also propose a simplified turbulence geometric model to evaluate the GLAO performance with different turbulence-profile-related parameters. With the above methods, we analyse the required number of deformable mirror (DM) actuators, the number, position and magnitude of LGS and explore the DM tilt-conjugation problem for this GLAO system.

Citation

Peng, J., Basden, A., & Osborn, J. (2018). Ground-layer adaptive-optics system modelling for the ChineseLarge Optical/Infrared Telescope. Monthly Notices of the Royal Astronomical Society, 479(1), 829-843. https://doi.org/10.1093/mnras/sty1507

• Journal Article Type: Article
• Acceptance Date: Jun 6, 2018
• Online Publication Date: Jul 5, 2018
• Publication Date: Jul 5, 2018
• Deposit Date: Aug 20, 2018
• Publicly Available Date: Aug 22, 2018
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Royal Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 479
• Issue: 1
• Pages: 829-843
• DOI: https://doi.org/10.1093/mnras/sty1507
• Public URL: https://durham-repository.worktribe.com/output/1322511