Improving adaptive optics reconstructions with a deep learning approach

Abstract

The use of techniques such as adaptive optics is mandatory when performing astronomical observation from ground based telescopes, due to the atmospheric turbulence effects. In the latest years, artificial intelligence methods were applied in this topic, with artificial neural networks becoming one of the reconstruction algorithms with better performance. These algorithms are developed to work with Shack-Hartmann wavefront sensors, which measures the turbulent profiles in terms of centroid coordinates of their subapertures and the algorithms calculate the correction over them. In this work is presented a Convolutional Neural Network (CNN) as an alternative, based on the idea of calculating the correction with all the information recorded by the Shack-Hartmann, for avoiding any possible loss of information. With the support of the Durham Adaptive optics Simulation Platform (DASP), simulations were performed for the training and posterior testing of the networks. This new CNN reconstructor is compared with the previous models of neural networks in tests varying the altitude of the turbulence layer and the strength of the turbulent profiles. The CNN reconstructor shows promising improvements in all the tested scenarios.