Dynamic Artificial Neural Networks for Centroid Prediction in Astronomy
Motivation for this research is the real-time restoration of faint astronomical images through turbulence over a large field-of-view. A simulation platform was developed to predict the centroid of a science object, convolved through multiple perturbation fields, and projected on to an image plane. Centroid data were selected from various source and target locations and used to train an artificial neural network to estimate centroids over a spatial grid, defined on the image plane. The capability of the network to learn to predict centroids over new target locations was assessed using a priori centroid data corresponding to selected grid locations. Various distortion fields were used in training and simulating the network including data collected from observation runs at a local observatory. Results from this work provide the basis for extensions and application to modal tomography.