Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks
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 res...
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/7/1028 |
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| author | Saúl Pérez-Fernández Alejandro Buendía-Roca Carlos González-Gutiérrez Francisco García-Riesgo Javier Rodríguez-Rodríguez Santiago Iglesias-Alvarez Julia Fernández-Díaz Francisco Javier Iglesias-Rodríguez |
| author_facet | Saúl Pérez-Fernández Alejandro Buendía-Roca Carlos González-Gutiérrez Francisco García-Riesgo Javier Rodríguez-Rodríguez Santiago Iglesias-Alvarez Julia Fernández-Díaz Francisco Javier Iglesias-Rodríguez |
| author_sort | Saúl Pérez-Fernández |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-7dc561c93b5a4ec8b0f6b62e27b7d8a72025-08-20T02:09:19ZengMDPI AGMathematics2227-73902025-03-01137102810.3390/math13071028Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural NetworksSaúl Pérez-Fernández0Alejandro Buendía-Roca1Carlos González-Gutiérrez2Francisco García-Riesgo3Javier Rodríguez-Rodríguez4Santiago Iglesias-Alvarez5Julia Fernández-Díaz6Francisco Javier Iglesias-Rodríguez7Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainInstituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, SpainNeural 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.https://www.mdpi.com/2227-7390/13/7/1028machine learningneural networksastronomyopticsinstrumentationpyramid wavefront sensor |
| spellingShingle | Saúl Pérez-Fernández Alejandro Buendía-Roca Carlos González-Gutiérrez Francisco García-Riesgo Javier Rodríguez-Rodríguez Santiago Iglesias-Alvarez Julia Fernández-Díaz Francisco Javier Iglesias-Rodríguez Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks Mathematics machine learning neural networks astronomy optics instrumentation pyramid wavefront sensor |
| title | Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks |
| title_full | Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks |
| title_fullStr | Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks |
| title_full_unstemmed | Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks |
| title_short | Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks |
| title_sort | open loop wavefront reconstruction with pyramidal sensors using convolutional neural networks |
| topic | machine learning neural networks astronomy optics instrumentation pyramid wavefront sensor |
| url | https://www.mdpi.com/2227-7390/13/7/1028 |
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