Identifying Host Galaxies of Extragalactic Radio Emission Structures using Machine Learning

Abstract

This paper presents an automatic multi-band source cross-identification method based on deep learning to identify the hosts of extragalactic radio emission structures. The aim is to satisfy the increased demand for automatic radio source identification and analysis of large-scale survey data from next-generation radio facilities such as the Square Kilometre Array and the Next Generation Very Large Array. We demonstrate a 97% overall accuracy in distinguishing quasi-stellar objects, galaxies and stars using their optical morphologies plus their corresponding mid-infrared information by training and testing a convolutional neural network on Pan-STARRS imaging and WISE photometry. Compared with an expert-evaluated sample, we show that our approach has 95% accuracy at identifying the hosts of extended radio components. We also find that improving radio core localization, for instance by locating its geodesic center, could further increase the accuracy of locating the hosts of systems with a complex radio structure, such as C-shaped radio galaxies. The framework developed in this work can be used for analyzing data from future large-scale radio surveys.