@inproceedings{27e59a3d0cdc46a8b8b0f519b4e51e37,
title = "Efficient asterism selection for wide field adaptive optics systems with TIPTOP",
abstract = "Wide Field Adaptive Optics (WFAO) systems play a pivotal role in enhancing the imaging and spectroscopic capabilities of astronomical telescopes. In the context of MCAO, with MAVIS (Multi-conjugate Adaptive Optics Assisted Visible Imager and Spectrograph) as a representative example, the careful selection of an appropriate NGS (Natural Guide Star) asterism, consisting of NGSs within the technical field of view (FoV) of the adaptive optics system is crucial. We present our solution to the problem, which was developed as an addition to the TIPTOP simulation framework. Since our goal is to provide astronomers an effective tool for observation planning, we did focus on the performance of our implementation, which greatly benefits from GPU availability, to compute the result within seconds.",
keywords = "Asterism, Exposure Time Calculator, Machine Learning, MAVIS, MCAO, Natural Guide Star, Neural Network, Simulation, Wide Field Adaptive Optics",
author = "Fabio Rossi and Guido Agapito and C{\'e}dric Plantet and Beno{\^i}t Neichel and Fran{\c c}ois Rigaut",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Adaptive Optics Systems IX 2024 ; Conference date: 16-06-2024 Through 22-06-2024",
year = "2024",
doi = "10.1117/12.3020159",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jackson, \{Kathryn J.\} and Dirk Schmidt and Elise Vernet",
booktitle = "Adaptive Optics Systems IX",
address = "United States",
}