Experience with Artificial Neural Networks Applied in Multi-object Adaptive Optics. (16th September 2019)

• Record Type: Journal Article
• Authors: Suárez Gómez, Sergio Luis; González-Gutiérrez, Carlos; Alonso, Enrique Díez; Santos, Jesús Daniel; Rodríguez, María Luisa Sánchez; Morris, Tim; Osborn, James; Basden, Alastair; Bonavera, Laura; González, Joaquín González-Nuevo; de Cos Juez, Francisco Javier
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 131:Number 1004(2019)
• Publication Date: 2019-09-16
• Subjects: instrumentation: adaptive optics -- methods: data analysis -- techniques: image processing; Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/ab1ebb
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: 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.