Application of AI technology in pulsar candidate identification  

作　　者：Wanqiong Wang Jie Wang Xinchen Ye Yazhou Zhang Jia Li Xu Du Wenna Cai Han Wu Ting Zhang Yuyue Jiao 

机构地区：[1]Xinjiang Astronomical Observatory,Chinese Academy of Sciences,Urumqi 830011,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]National Astronomical Data Center,Beijing 100101,China

出　　处：《Astronomical Techniques and Instruments》2025年第1期27-43,共17页天文技术与仪器(英文)

基　　金：supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502);the National Natural Science Foundation of China(NSFC)(12173077);the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112);the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01);China National Astronomical Data Center(NADC);the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS);Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。

摘　　要：As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.

关 键 词：AI technology Candidate identification Machine learning Neural networks 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P145.6[自动化与计算机技术—控制科学与工程]