Overview and Choice of Artificial Intelligence Approaches for Night-Time Adaptive Optics Reconstruction

Abstract

Adaptive optics (AO) is one of the most relevant systems for ground-based telescopes image correction. AO is characterized by demanding computational systems that must be able to quickly manage large amounts of data, trying to make all the calculations needed the closest to real-time. Furthermore, next generations of telescopes that are already being constructed will demand higher computational requirements. For these reasons, artificial neural networks (ANNs) have recently become one alternative to commonly used tomographic reconstructions based on several algorithms as the least-squares method. ANNs have shown its capacity to model complex physical systems, as well as predicting values in the case of nocturnal AO where some models have already been tested. In this research, a comparison in terms of quality of the outputs given and computational time needed is presented between three of the most common ANN topologies used nowadays, to obtain the one that fits better these AO systems requirements. Multi-layer perceptron (MLP), convolutional neural networks (CNN) and fully convolutional neural networks (FCN) are considered. The results presented determine the way forward for the development of reconstruction systems based on ANNs for future telescopes, as the ones being under construction for solar observations.