Solar adaptive optics: specificities, lessons learned, and open alternatives

Montilla, I.; Mariño, José María Solé; Ramos, A. Asensio; Collados, M.; Montoya, Luzma; Tallon, Michel

doi:10.1117/12.2233652

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Working on extended and low-contrast objects the Field Of View (FOV) has to be large enough to contain structure for the correlation algorithm to work robustly (~8-10 arcsec). With such FOV, the anisoplanatism affects the measurements of the correlating SH-WFS decreasing the sensitivity to wavefront distortions introduced at large heights above the telescope aperture [4]. CNNs have the potential to perform reconstruction using as reference images with smaller FOVs, alleviating this effect and increasing the performance.…”

Section: Neural Network In Aomentioning

confidence: 99%

“…However, the observation of the sun, especially in the optic spectrum, have gained a lot of interest during the last years [2]. The development of the European Solar Telescope (EST) [3], a 4 meters solar telescope has created new challenges regarding not only the engineering, but the control systems to handle this new generation of solar telescopes [4].…”

Section: Introductionmentioning

confidence: 99%

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An approach using deep learning for tomographic reconstruction in solar observation

Gómez¹,

Gutiérrez²,

Lasheras³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

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Solar observations with large telescopes have to face numerous challenges for its implementation. Adapting the current reconstructor systems of adaptive optics, which were developed for night observations to remove the atmospheric turbulence, is one of them. Neural networks were proved as a great solution in different fields, as image processing or in tomographic reconstruction. In this paper, possible uses of neural networks in solar adaptive optics will be explored, providing some early results of their application to different fields of adaptive optics.

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Section: Neural Network In Aomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An approach using deep learning for tomographic reconstruction in solar observation

Gómez¹,

Gutiérrez²,

Lasheras³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

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show abstract

“…However, the use of AO is not restrained to nocturnal observations. Research on processes that take place in the sun have an enormous interest, since it strongly affects life on Earth [3]. The extremely dynamic behavior of the solar atmosphere influences the Earth.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Gómez

González-Gutiérrez

Riesgo

et al. 2019

Sensors

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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.

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