Deep learning for multi-star recognition in optical turbulence

Abstract

In the presence of high-strength turbulence, it is difficult to recognize close stars in ground-based imaging systems. Although adaptive optics could be helpful to reconstruct such images, there are always some remaining uncorrected phases for different turbulence conditions that could affect the recognition of close stars. Considering this, we have introduced a classification-based method by using a deep learning network to distinguish such star systems without correcting the wavefronts. To this aim, we have configured a Convolutional Neural Network (CNN). Five turbulence models are used to generate a dataset that includes thousands of images. Moreover, four metrics have been utilized to evaluate the CNN after the learning process. The accuracy of the network was upper than 80% for all of the turbulence models. The comparison of the five turbulence models is presented in detail, based on these metrics, and the robustness of the deep learning network is reported.