Gradient-Guided Attentional Network for Radio Transient Localization With the Cluster-Feed Telescope

Large, single-dish radio telescopes with high sensitivities are ideal for detecting faint radio transients (RTs). However, single-dish radio telescopes possess a limited angular resolution, which limits their accuracy in localizing objects. In this article, we propose to improve the localization accuracy of the RT by exploring the 3-D focal field distributions (3DFFDs) of the dish reflector with a gradient-guided attentional network (GGAN). The LSTM-based attention block of the GGAN achieves the task-oriented adaptive recalibration of 3DFFD features by exploring the significant properties and spatial dependencies of 3DFFD. In addition, a gradient-guided approach is being developed to improve the attention block performance under varying incident angles. The proposed attention mechanism is applied to the convolutional neural network in order to reconstruct 3DFFDs and perceive RT positions based on the reconstructed results. Simulation results indicate that the technique can enable the precise localization of RTs. Moreover, the proposed solution improves the telescope’s instantaneous field of view (FOV) compared to a sky survey with the traditional cluster feed telescope.