Fully Convolutional Approaches for Numerical Approximation of Turbulent Phases in Solar Adaptive Optics

Francisco García Riesgo, Sergio Luis Suárez Gómez, Enrique Díez Alonso, Carlos González-Gutiérrez and Jesús Daniel Santos
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Francisco García Riesgo: Department of Physics, University of Oviedo, 33007 Oviedo, Spain
Sergio Luis Suárez Gómez: Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain
Enrique Díez Alonso: Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain
Carlos González-Gutiérrez: Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain
Jesús Daniel Santos: Department of Physics, University of Oviedo, 33007 Oviedo, Spain

Mathematics, 2021, vol. 9, issue 14, 1-20

Abstract: Information on the correlations from solar Shack–Hartmann wavefront sensors is usually used for reconstruction algorithms. However, modern applications of artificial neural networks as adaptive optics reconstruction algorithms allow the use of the full image as an input to the system intended for estimating a correction, avoiding approximations and a loss of information, and obtaining numerical values of those correlations. Although studied for night-time adaptive optics, the solar scenario implies more complexity due to the resolution of the solar images potentially taken. Fully convolutional neural networks were the technique chosen in this research to address this problem. In this work, wavefront phase recovery for adaptive optics correction is addressed, comparing networks that use images from the sensor or images from the correlations as inputs. As a result, this research shows improvements in performance for phase recovery with the image-to-phase approach. For recovering the turbulence of high-altitude layers, up to 93% similarity is reached.

Keywords: fully convolutional neural networks; artificial intelligence; artificial neural networks; adaptive optics; solar physics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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