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Experience with artificial neural networks applied in multi-object adaptive optics.

Suárez Gómez, Sergio Luis and González-Gutiérrez, Carlos and Alonso, Enrique Díez and Santos, Jesús Daniel and Rodríguez, María Luisa Sánchez and Morris, Tim and Osborn, James and Basden, Alastair and Bonavera, Laura and González, Joaquín González-Nuevo and de Cos Juez, Francisco Javier (2019) 'Experience with artificial neural networks applied in multi-object adaptive optics.', Publications of the Astronomical Society of the Pacific., 131 (1004). p. 108012.


The use of artificial Intelligence techniques has become widespread in many fields of science, due to their ability to learn from real data and adjust to complex models with ease. These techniques have landed in the field of adaptive optics, and are being used to correct distortions caused by atmospheric turbulence in astronomical images obtained by ground-based telescopes. Advances for multi-object adaptive optics are considered here, focusing particularly on artificial neural networks, which have shown great performance and robustness when compared with other artificial intelligence techniques. The use of artificial neural networks has evolved to the extent of the creation of a reconstruction technique that is capable of estimating the wavefront of light after being deformed by the atmosphere. Based on this idea, different solutions have been proposed in recent years, including the use of new types of artificial neural networks. The results of techniques based on artificial neural networks have led to further applications in the field of adaptive optics, which are included in here, such as the development of new techniques for solar observation or their application in novel types of sensors.

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Publisher statement:Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Date accepted:01 May 2019
Date deposited:08 October 2019
Date of first online publication:16 September 2019
Date first made open access:08 October 2019

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