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

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1630
Author(s):  
Francisco García Riesgo ◽  
Sergio Luis Suárez Gómez ◽  
Enrique Díez Alonso ◽  
Carlos González-Gutiérrez ◽  
Jesús Daniel Santos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Altitude ◽  
Adaptive Optics ◽  
Numerical Approximation ◽  
Reconstruction Algorithms ◽  
Wavefront Sensors ◽  
Night Time ◽  
Phase Recovery ◽  
Fully Convolutional Neural Networks

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.

Tomographic reconstructor for multi-object adaptive optics using artificial neural networks

2012 ◽  
Author(s):  
Dani Guzman ◽  
Alexandre T. Mello ◽  
James Osborn ◽  
Francisco J. De Cos ◽  
Marlon Gómez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Optics ◽  
Artificial Neural

Compensating atmospheric turbulence with CNNs for defocused pupil image wavefront sensors

2020 ◽  
Author(s):  
Sergio Luis Suárez Gómez ◽  
Carlos González-Gutiérrez ◽  
Juan Díaz Suárez ◽  
Juan José Fernández Valdivia ◽  
José Manuel Rodríguez Ramos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Structural Similarity ◽  
Detailed Comparison ◽  
Optical Measurements ◽  
Wavefront Sensors ◽  
Machine Learning Model ◽  
Reconstruction Software ◽  
Astronomical Images ◽  
Zernike Modes

Abstract Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work, computational results are presented for a modified curvature sensor, the tomographic pupil image wavefront sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional neural networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (root mean square error, mean structural similarity and Strehl ratio). The reconstructed wavefronts from both techniques are compared for wavefronts of 153 Zernike modes. For this case, a detailed comparison and grid search to find the most suitable neural network is performed, searching between multi-layer perceptron, CNN and recurrent networks topologies. In general, the best network was a CNN trained for TPI-WFS reconstruction, achieving better performance than the reconstruction software from TPI-WFS in most of the turbulent profiles, but the most significant improvements were found for higher turbulent profiles that have the lowest r0 values.

scholarly journals Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2233 ◽  
Author(s):  
Sergio Luis Suárez Gómez ◽  
Carlos González-Gutiérrez ◽  
Francisco García Riesgo ◽  
Maria Luisa Sánchez Rodríguez ◽  
Francisco Javier Iglesias Rodríguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Adaptive Optics ◽  
Reconstruction Algorithm ◽  
Reconstruction Technique ◽  
Solar Observation ◽  
The Sun ◽  
Reconstruction Algorithms ◽  
Turbulence Effects ◽  
Atmospheric Turbulence Effects

Correcting atmospheric turbulence effects in light with Adaptive Optics is necessary, since it produces aberrations in the wavefront of astronomical objects observed with telescopes from Earth. These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. In the context of solar observation, the usage of Adaptive Optics on solar differs from nocturnal operations, bringing up a challenge to correct the image aberrations. In this work, a convolutional approach is given to address this issue, considering SCAO configurations. A reconstruction algorithm is presented, “Shack-Hartmann reconstruction with deep learning on solar–prototype” (proto-HELIOS), to correct on fixed solar images, achieving an average 85.39% of precision in the reconstruction. Additionally, results encourage to continue working with these techniques to achieve a reconstruction technique for all the regions of the sun.

scholarly journals Performance limits of adaptive-optics/high-contrast imagers with pyramid wavefront sensors

2020 ◽  
Vol 495 (4) ◽  
pp. 4380-4391
Author(s):  
Carlos M Correia ◽  
Olivier Fauvarque ◽  
Charlotte Z Bond ◽  
Vincent Chambouleyron ◽  
Jean-François Sauvage ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Large Scale ◽  
Near Infrared ◽  
Dynamic Control ◽  
Diffraction Theory ◽  
Peak Performance ◽  
High Contrast ◽  
Reconstruction Algorithms ◽  
Performance Limits ◽  
Wavefront Sensors

ABSTRACT Advanced adaptive-optics (AO) systems will likely utilize pyramid wavefront sensors (PWFSs) over the traditional Shack–Hartmann sensor in the quest for increased sensitivity, peak performance and ultimate contrast. Here, we explain and quantify the PWFS theoretical limits as a means to highlight its properties and applications. We explore forward models for the PWFS in the spatial-frequency domain: these prove useful because (i) they emanate directly from physical-optics (Fourier) diffraction theory; (ii) they provide a straightforward path to meaningful error breakdowns; (iii) they allow for reconstruction algorithms with $O (n\, \log(n))$ complexity for large-scale systems; and (iv) they tie in seamlessly with decoupled (distributed) optimal predictive dynamic control for performance and contrast optimization. All these aspects are dealt with here. We focus on recent analytical PWFS developments and demonstrate the performance using both analytic and end-to-end simulations. We anchor our estimates on observed on-sky contrast on existing systems, and then show very good agreement between analytical and Monte Carlo performance estimates on AO systems featuring the PWFS. For a potential upgrade of existing high-contrast imagers on 10-m-class telescopes with visible or near-infrared PWFSs, we show, under median conditions at Paranal, a contrast improvement (limited by chromatic and scintillation effects) of 2×–5× when just replacing the wavefront sensor at large separations close to the AO control radius where aliasing dominates, and of factors in excess of 10× by coupling distributed control with the PWFS over most of the AO control region, from small separations starting with an inner working angle of typically 1–2 λ/D to the AO correction edge (here 20 λ/D).

scholarly journals Experience with Artificial Neural Networks Applied in Multi-object Adaptive Optics

2019 ◽  
Vol 131 (1004) ◽  
pp. 108012 ◽  
Author(s):  
Sergio Luis Suárez Gómez ◽  
Carlos González-Gutiérrez ◽  
Enrique Díez Alonso ◽  
Jesús Daniel Santos ◽  
María Luisa Sánchez Rodríguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Optics ◽  
Artificial Neural
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