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.
Highlights
Adaptive optics (AO) has become an essential tool for improving the quality of images obtained by ground-based telescopes (Roddier 1999)
An average of the recall of 10,000 samples was used, with a standard deviation below 1%. These results indicate that in the case of extremely large telescopes, with a large number of Shack-Hartmann wavefront sensor (SH-wavefront sensor (WFS)) and subapertures, the use of graphics processing units (GPU) is a suitable solution for managing that amount of data
With modern large telescopes a huge amount of data will need to be processed and the application of Artificial intelligence (AI) seems the optimal approach to solving this problem
Summary
Adaptive optics (AO) has become an essential tool for improving the quality of images obtained by ground-based telescopes (Roddier 1999). The newest and nearfuture telescopes are increasing in size and, in the amount of data retrieved (Rigaut 2002) This implies a big challenge for techniques such as AO, as the processing of large amounts of data slows the calculations of the corrections, which should really be performed in real time (Dipper et al 2013). AI techniques are widely known in recent times as powerful tools in the handling of big data and the mathematical modelling of physical systems (Russell & Norvig 2016) This is mainly due to the flexibility of such techniques, which rely on the ability of AI to successfully learn directly from the data of the real problem, allowing AI to be applied in many different research fields, such as speech or image recognition (Graves et al 2013; Krizhevsky et al 2012).
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