Identifying Host Galaxies of Extragalactic Radio Emission Structures using Machine Learning
作者机构：National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of SciencesBeijing 100049China Institute for Frontiers in Astronomy and AstrophysicsBeijing Normal UniversityBeijing 102206China
出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)
年 卷 期：2023年第23卷第7期
页 面：139-153页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070401[理学-天体物理] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by grants from the National Natural Science Foundation of China(Nos.11973051,12041302) funded by Chinese Academy of Sciences President’s International Fellowship Initiative.Grant No.2019PM0017
主 题：techniques:image processing surveys methods:data analysis
摘 要：This paper presents an automatic multi-band source cross-identification method based on deep learning to identify the hosts of extragalactic radio emission *** 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 *** 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 *** with an expert-evaluated sample,we show that our approach has 95%accuracy at identifying the hosts of extended radio *** 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 *** framework developed in this work can be used for analyzing data from future large-scale radio surveys.
维普期刊数据库
同方期刊数据库