Covariance-based analytical algorithm to predict the performance of tomographic AO systems

Yoshito Ono; Masayuki Akiyama; Koki Terao; Yosuke Minowa; Hajime Ogane; Shin Oya

doi:10.1117/12.2628873

29 August 2022 Covariance-based analytical algorithm to predict the performance of tomographic AO systems

Yoshito Ono, Masayuki Akiyama, Koki Terao, Yosuke Minowa, Hajime Ogane, Shin Oya

Proceedings Volume 12185, Adaptive Optics Systems VIII; 121850F (2022) https://doi.org/10.1117/12.2628873
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Abstract

We develop a covariance-based analytical algorithm to efficiently predict the performance of complex tomographic AO systems based Shack-Hartmann WFSs (SH-WFS). The algorithm produces a predicted point spread function (PSF) and a decomposed wavefront error for each error term and is implemented using GPU and CUDA libraries for efficient computation. In this paper, we introduce the basis of our algorithm and show the prediction results, computational speed, and comparison with end-to-end simulations for the ULTIMATE-SUBARU GLAO and LTAO systems as test cases.

Conference Presentation

Citation Download Citation

Yoshito Ono, Masayuki Akiyama, Koki Terao, Yosuke Minowa, Hajime Ogane, and Shin Oya "Covariance-based analytical algorithm to predict the performance of tomographic AO systems", Proc. SPIE 12185, Adaptive Optics Systems VIII, 121850F (29 August 2022); https://doi.org/10.1117/12.2628873

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