Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks

Saúl Pérez-Fernández (), Alejandro Buendía-Roca, Carlos González-Gutiérrez, Francisco García-Riesgo, Javier Rodríguez-Rodríguez, Santiago Iglesias-Alvarez, Julia Fernández-Díaz and Francisco Javier Iglesias-Rodríguez
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Saúl Pérez-Fernández: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Alejandro Buendía-Roca: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Carlos González-Gutiérrez: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Francisco García-Riesgo: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Javier Rodríguez-Rodríguez: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Santiago Iglesias-Alvarez: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Julia Fernández-Díaz: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain
Francisco Javier Iglesias-Rodríguez: Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain

Mathematics, 2025, vol. 13, issue 7, 1-16

Abstract: Neural networks have significantly advanced adaptive optics systems for telescopes in recent years. Future adaptive optics systems, especially for extremely large telescopes, are expected to predominantly employ pyramid wavefront sensors, which offer good sensitivity but suffer from a non-linear response under certain conditions. This non-linearity limits the performance of traditional linear reconstruction methods, such as matrix–vector multiplication, leading to suboptimal performance. Convolutional Neural Networks offer a promising alternative, as they can model complex non-linear relationships and extract spatial patterns from sensor images. While CNN-based reconstruction has shown success in closed-loop systems, this study investigates their application in open-loop wavefront reconstruction. A custom network architecture and training strategy are developed, using realistic training data from end-to-end atmospheric turbulence simulations. CNNs are trained to reconstruct Zernike polynomial coefficients representing optical aberrations, enabling a tomographic estimation of turbulence. The proposed approach demonstrates significant improvements over conventional open-loop methods, underscoring the potential of CNNs to enhance wavefront reconstruction in next-generation AO systems.

Keywords: machine learning; neural networks; astronomy; optics; instrumentation; pyramid wavefront sensor; atmospheric simulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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