Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning  

作　　者：Yu-Xin Wang Shang-Jie Jin Tian-Yang Sun Jing-Fei Zhang Xin Zhang 王钰鑫;金上捷;孙天阳;张敬飞;张鑫(Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences,Northeastern University,Shenyang 110819,China;Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education),Northeastern University,Shenyang 110819,China;National Frontiers Science Center for Industrial Intelligence and Systems Optimization,Northeastern University,Shenyang 110819,China)

机构地区：[1]Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences,Northeastern University,Shenyang 110819,China [2]Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education),Northeastern University,Shenyang 110819,China [3]National Frontiers Science Center for Industrial Intelligence and Systems Optimization,Northeastern University,Shenyang 110819,China

出　　处：《Chinese Physics C》2024年第12期230-242,共13页中国物理C（英文版）

基　　金：Supported by the National SKA Program of China(2022SKA0110200,2022SKA0110203);the National Natural Science Foundation of China(12473001,11975072,11875102,11835009);the National 111 Project(B16009)。

摘　　要：Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)signals.The rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA.In this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)mergers.We simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector noise.We found that the GW events in the first and second observation runs could all be clearly and rapidly identified.For the third observing run,approximately 80% of the GW events could be identified.In contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive analysis.In conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

关 键 词：gravitational waves deep learning binary black holes time-frequency domain signals U-Net algorithm 

分 类 号：P14[天文地球—天体物理] TP18[天文地球—天文学]