Enhancing Open-Loop Wavefront Prediction in Adaptive Optics through 2D-LSTM Neural Network Implementation

Abstract

Adaptive optics (AO) is a technique with an important role in image correction on ground-based telescopes through the deployment of specific optical instruments and various control methodologies. The synergy between these instruments and control techniques is paramount for capturing sharper and more accurate images. This technology also plays a crucial role in other applications, including power and information systems, where it compensates for thermal distortion caused by radiation. The integration of neural networks into AO represents a significant step towards achieving optimal image clarity. Leveraging the learning potential of these models, researchers can amplify control strategies to counteract atmospheric distortions effectively. Neural networks in AO not only produce results on par with conventional systems but also proffer benefits in cost-efficiency and streamlined implementation. This study explores the potential of an artificial neural network (ANN) as a nonlinear predictor for open-loop wavefronts. Expanding on prior evidence showing advantages over classic methods, this investigation boosts prediction accuracy through the integration of advanced models and machine learning approaches.