Global piston restoration of segmented mirrors with recurrent neural networks

Abstract

Recurrent neural networks are usually used for processing sequential data. They have been employed in this paper to deal with the sequence of diffraction subimages created by every intersection from a segmented mirror. Every subimage is first processed by a convolutional neural network that extracts a set of features from each of them. It was attained superior prediction accuracy with the recurrent approach than with convolution layers alone. Furthermore, a consistency test was added to detect wrong predictions before computing the global piston values. The final system predicts global piston values with rms = 7.34 nm, high reliability, and capture range of [ − 21 λ , 21 λ ]. Atmospheric seeing, polishing and tip-tilt residual errors were also added in the simulations.