Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow  

作　　者：孙天阳 熊春雨 金上捷 王钰鑫 张敬飞 张鑫 Tian-Yang Sun;Chun-Yu Xiong;Shang-Jie Jin;Yu-Xin Wang;Jing-Fei Zhang;Xin Zhang(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4期240-251,共12页中国物理C（英文版）

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

摘　　要：Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave(GW)signals,thereby exerting a notable impact on the processing of GW data.The inference of GW parameters,crucial for GW astronomy research,is particularly susceptible to such interference.In this study,we pioneer the utilization of a temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters,seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency domains.Remarkably,our findings indicate that the accuracy of this inference method is comparable to that of traditional non-glitch sampling techniques.Furthermore,our approach exhibits a greater efficiency,boasting processing times on the order of milliseconds.In conclusion,the application of a normalizing flow emerges as pivotal in handling GW signals affected by transient noises,offering a promising avenue for enhancing the field of GW astronomy research.

关 键 词：gravitational wave glitch non-Gaussian and transient noise normalizing flow machine learning likelihood-free parameter inference 

分 类 号：P142.84[天文地球—天体物理]