Presentation + Paper
27 August 2024 TipTop: toward a single tool for all ELT instrument’s PSF prediction
Author Affiliations +
Abstract
To facilitate easy prediction and estimation of adaptive optics performance, we have created a fast algorithm named TipTop. This algorithm generates the expected AO point spread function (PSF) for any existing AO observing mode (SCAO, LTAO, MCAO, GLAO) and any set of atmospheric conditions. Developed in Python, TipTop is based on an analytical approach, with simulations performed in the Fourier domain, enabling very fast computation times (less than a second per PSF) and efficient exploration of the extensive parameter space. TipTop can be used for several applications, from assisting in the observation preparation with the exposure time calculator (ETC), to providing PSF models for post-processing. TipTop can also be used to help users in selecting the best NGSs asterism and optimizing their observation. Over the past years, the code has been intensively tested against other simulation tools, showing very good agreements. TipTop is also currently deployed for VLT instruments, as proof of concepts in preparation of the ELT. The code is available here: https://tiptop.readthedocs.io/en/main/, and we encourage all future observers of the ELT to test it and provide feedback.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Benoit Neichel, Guido Agapito, Arseniy Kuznetsov, Fabio Rossi, Cédric Plantet, Carlo F. Manara, Romain Fétick, Alice Concas, Joël Vernet, Olivier Hainaut, Anne-Laure Cheffot, Giulia Carlà, Jean-François Sauvage, Michele Cirasuolo, Paolo Padovani, Carlos Correia, Cedric Taissir Héritier, and Thierry Fusco "TipTop: toward a single tool for all ELT instrument’s PSF prediction", Proc. SPIE 13097, Adaptive Optics Systems IX, 130972Y (27 August 2024); https://doi.org/10.1117/12.3015061
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KEYWORDS
Point spread functions

Adaptive optics

Equipment

Calibration

Stars

Data modeling

Simulations

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