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27 August 2024 Closed–loop experimental validation using neural networks in a non-modulated pyramidal wavefront sensor
Camilo Weinberger, Benoit Neichel, Jorge Tapia, Esteban Vera
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Proceedings Volume 13097, Adaptive Optics Systems IX; 130970S (2024) https://doi.org/10.1117/12.3020024
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan
Abstract
The rise of extreme Large Telescopes (ELTs) poses challenges for high-resolution phase map reconstruction. Despite the pyramid wavefront sensor (PyWFS) promise, its inherent non-linearity is limiting. This study proposes techniques to enhance the non-modulated PyWFS linearity through deep learning, comparing convolutional Neural Networks (CNNs) models (Xception, WFNet, ConvNext) with the transformer model Global Context Vision Transformers (GCViT). Results favor transformers, highlighting CNN limitations near pupil borders. Experimental validation on the PULPOS optical bench underscores the GCViT robustness. Trained solely on simulated data under varied SNR and D/r0 conditions, our approach enables to accurately close the AO loop in a real system and leave behind the reconstruction paradigm based on the interaction matrix. We demonstrate the high performance of the GCViT in closed loop obtaining a Strehl ratio over 0.6 for strong turbulence and nearly 0.95 for weak turbulence on the PULPOS optical bench.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Camilo Weinberger, Benoit Neichel, Jorge Tapia, and Esteban Vera "Closed–loop experimental validation using neural networks in a non-modulated pyramidal wavefront sensor", Proc. SPIE 13097, Adaptive Optics Systems IX, 130970S (27 August 2024); https://doi.org/10.1117/12.3020024
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KEYWORDS
Adaptive optics
Wavefront sensors
Neural networks
Machine learning
Transformers
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