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29 August 2022 Exploration of convolutional neural networks to handle non-linearity estimation issues in pyramid wavefront sensors.
Camilo Weinberger, Felipe Guzmán, Jorge Tapia, Benoît Neichel, Esteban Vera
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Proceedings Volume 12185, Adaptive Optics Systems VIII; 1218588 (2022) https://doi.org/10.1117/12.2630099
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada
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Abstract
In this work, we evaluate a especially crafted deep convolutional neural network to provide with estimations of the wavefront aberration modes directly from pyramidal wavefront sensor (PyWFS) images. Overall, the use of deep neural networks allow to improve the estimation performance as well as the operational range of the PyWFS, especially when considering cases of strong turbulence or bad seeing ratios D0/r0. Our preliminary results provide with evidence that by using neural nets, instead of the classic linear estimation methods, we can obtain a low modulation sensitivity response while extending the linearity range of the PyWFS, reducing the residual variance by a factor of 1.6 when dealing with a r0 as low as a few centimeters.
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Camilo Weinberger, Felipe Guzmán, Jorge Tapia, Benoît Neichel, and Esteban Vera "Exploration of convolutional neural networks to handle non-linearity estimation issues in pyramid wavefront sensors.", Proc. SPIE 12185, Adaptive Optics Systems VIII, 1218588 (29 August 2022); https://doi.org/10.1117/12.2630099
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KEYWORDS
Wavefront sensors
Telescopes
Convolutional neural networks
Adaptive optics
Neural networks
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