Several telescopes include large Deformable Mirrors (DM) located directly inside the telescope. These adaptive telescopes trigger new constraints for the calibration of the Adaptive Optics (AO) systems as they usually offer no access to an artificial calibration source for the interaction matrix measurement. Moreover, the optical propagation between the DM and the Wave-Front Sensor (WFS) may evolve during the operation, resulting in mis-registrations that highly affect the AO performance and thus the scientific observation. They have to be measured and compensated, for instance by updating the calibration. A new strategy consists of estimating the mis-registrations and injecting them into synthetic models to generate noise-free interaction matrices. This pseudo-synthetic approach is the baseline for the Adaptive Optics Facility working with a Shack-Hartmann WFS and seems particularly suited for the future Extremely Large Telescope as the calibration will have to be regularly updated, for a large numbers of actuators. In this paper, the feasibility of a pseudo synthetic calibration with Pyramid WFS at the Large Binocular Telescope (LBT) is investigated. A synthetic model of the LBT AO systems is developed, and the procedure to adjust the mis-registrations parameters is introduced, extracting them from an experimental interaction matrix. We successfully tested an interaction matrix generated from the model on the real system in high-order AO mode. We recorded a slightly better performance with respect to the experimental one. This work demonstrates that a high accuracy calibration can be obtained using the pseudo synthetic approach with pyramid WFS.
SAXO+, the second-stage adaptive optics for SPHERE: NCPA compensation and dark-hole loop with a pyramid wavefront sensor
LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, Université Paris Cité, CY Cergy Paris Université, CNRS（巴黎天文台，PSL大学，索邦大学，巴黎西岱大学，塞吉-蓬图瓦兹大学，法国国家科学研究中心）；Université Lyon 1, ENS de Lyon, CNRS, CRAL（里昂第一大学，里昂高等师范学校，法国国家科学研究中心）；Départment d’astronomie de l’Université de Genève（日内瓦大学天文学系）；MPIA, Max-Planck-Institut für Astronomie（马克斯·普朗克天文学研究所）；INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（意大利国家天体物理研究所博洛尼亚空间天体物理与科学观测站）；Université Paris-Saclay, Institut d’Optique Graduate School, CNRS, Laboratoire Charles Fabry（巴黎萨克雷大学，光学研究生院，法国国家科学研究中心，夏尔·法布里实验室）；Université Grenoble Alpes, CNRS, IPAG（格勒诺布尔阿尔卑斯大学，法国国家科学研究中心，行星与地球物理学研究所）；Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange（蔚蓝海岸大学，蔚蓝海岸天文台，法国国家科学研究中心，拉格朗日实验室）；Aix-Marseille Univ., CNRS, CNES, LAM（艾克斯-马赛大学，法国国家科学研究中心，法国国家空间研究中心，天体物理实验室）
Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data
Taiyuan University of Technology（太原理工大学）；Durham University（杜伦大学）；Nanjing Institute of Astronomical Optics and Technology CAS（中国科学院南京天文光学技术研究所）；Silesian University of Technology（西里西亚工业大学）
The Giant Magellan Telescope high contrast adaptive optics phasing testbed (p-HCAT): lab tests of segment/petal phasing with a pyramid wavefront sensor and a holographic dispersed fringe sensor (HDFS) in turbulence
SynIM: a high-performance GPU-accelerated Python library for synthetic interaction and tomographic reconstruction matrices in next-generation adaptive optics
INAF – Osservatorio Astrofisico di Arcetri（意大利国家天体物理研究所-阿切尔塞天文台）；ADaptive Optics National laboratory in Italy (ADONI)（意大利自适应光学国家实验室）
Comparing realtime optical gain measurement and methods on MagAO-X
Wyant College of Optical Sciences, The University of Arizona（亚利桑那大学怀恩特光学学院）；Steward Observatory, The University of Arizona（亚利桑那大学斯图尔德天文台）；Subaru Telescope, National Observatory of Japan（日本国立天文台昴星团望远镜）；Astrobiology Center, National Institutes of Natural Sciences（国立自然科学研究所天体生物学中心）；Optical Sharpeners SAS, France（法国光学锐化SAS公司）；Leiden Observatory, Leiden University, The Netherlands（荷兰莱顿大学莱顿天文台）；Center for Computational Astrophysics, Flatiron Institute（平坦研究所计算天体物理学中心）；University of Michigan, Ann Arbor, MI（密歇根大学安娜堡分校）；Northrop Grumman Corporation, Rolling Meadows, Illinois（诺斯罗普·格鲁曼公司）；Draper Laboratory, Cambridge, Massachusetts（德雷珀实验室）；Starfire Optical Range, Kirtland Air Force Base, Albuquerque, New Mexico（空军基地星光光学测试场）；Earth & Planets Laboratory, Carnegie Science, Washington DC（卡内基科学地球与行星实验室）
Towards on-sky adaptive optics control using reinforcement learning
Lappeenranta–Lahti University of Technology（拉彭兰塔-拉赫蒂工业大学）；European Southern Observatory（欧洲南方天文台）；University of Helsinki（赫尔辛基大学）；University of Arizona（亚利桑那大学）；Wyant College of Optical Science, University of Arizona（亚利桑那大学怀恩特光学科学学院）；Astrobiology Center, National Institutes of Natural Sciences（自然科学研究机构天体生物学中心）；National Astronomical Observatory of Japan（日本国立天文台）
Making the unmodulated pyramid wavefront sensor smart II. First on-sky demonstration of extreme adaptive optics with deep learning
Leiden Observatory, Leiden University（莱顿大学莱顿天文台）；Center for Computational Astrophysics, Flatiron Institute（弗拉铁隆研究所计算天体物理中心）；Wyant College of Optical Sciences, The University of Arizona（亚利桑那大学怀恩特光学科学学院）；Subaru Telescope, National Observatory of Japan, National Institutes of Natural Sciences（日本自然科学研究机构国立天文台昴星团望远镜）；Astrobiology Center, National Institutes of Natural Sciences（日本自然科学研究机构天体生物学中心）；Northrop Grumman Corporation（诺斯罗普·格鲁曼公司）；Draper Laboratory（德雷珀实验室）；Starfire Optical Range（星火光学靶场）
