Handling class imbalance of radio frequency interference in deep learning-based fast radio burst search pipelines using a deep convolutional generative adversarial network  

作　　者：Wenlong Du Yanling Liu Maozheng Chen 

机构地区：[1]Xinjiang Astronomical Observatory,Chinese Academy of Sciences,Urumqi 830011,China [2]Xinjiang Key Laboratory of Microwave Technology,Urumqi 830011,China [3]Key Laboratory of Radio Astronomy and Technology,Chinese Academy of Sciences,Beijing 100101,China

出　　处：《Astronomical Techniques and Instruments》2025年第1期10-15,共6页天文技术与仪器(英文)

基　　金：supported by the Chinese Academy of Science"Light of West China"Program(2022-XBQNXZ-015);the National Natural Science Foundation of China(11903071);the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China and administered by the Chinese Academy of Sciences。

摘　　要：This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.

关 键 词：Fast radio burst Deep convolutional generative adversarial network Class imbalance Radio frequency interference Deep learning 

分 类 号：P162[天文地球—天文学]