Gravitational wave search by time-scale-recursive denoising and matched filtering  

作　　者：Cunliang Ma Chenyang Ma Zhoujian Cao Mingzhen Jia 

机构地区：[1]School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou,341000,China [2]Institute for Frontiers in Astronomy and Astrophysics,Beijing Normal University,Beijing,102206,China [3]School of Fundamental Physics and Mathematical Sciences,Hangzhou Institute for Advanced Study,UCAS,Hangzhou,310024,China [4]Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control,Ganzhou,341000,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2024年第12期166-182,共17页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the Gravitational-Wave Open Science Center,a service of LIGO Laboratory,the LIGO Scientific Collaboration,and the Virgo Collaboration;supported by the National Key Research and Development Program of China (Grant No.2021YFC2203001);the National Natural Science Foundation of China (Grants Nos.11920101003,12021003,12364024,and 11864014);the Natural Science Foundation of Jiangxi (Grant Nos.20224BAB211012,and 20224BAB201023)。

摘　　要：In our previous work [Physical Review D,2024,109(4):043009],we introduced MSNRnet,a framework integrating deep learning and matched filtering methods for gravitational wave(GW) detection.Compared with end-to-end classification methods,MSNRnet is physically interpretable.Multiple denoising models and astrophysical discrimination models corresponding to different parameter space were operated independently for the template prediction and selection.But the MSNRnet has a lot of computational redundancy.In this study,we propose a new framework for template prediction,which significantly improves our previous method.The new framework consists of the recursive application of denoising models and waveform classification models,which solve the problem of computational redundancy.The waveform classification network categorizes the denoised output based on the signal's time scale.To enhance the denoising performance for long-time-scale data,we upgrade the denoising model by incorporating Transformer and ResNet modules.Furthermore,we introduce a novel training approach that allows for the simultaneous training of the denoising network and waveform classification network,eliminating the need for manual annotation of the waveform dataset required in our previous method.Real-data analysis results demonstrate that our new method decreases the false alarm rate by approximately 25%,boosts the detection rate by roughly 5%,and slashes the computational cost by around 90%.The new method holds potential for future application in online GW data processing.

关 键 词：gravitational wave matched filtering deep learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P142.84[自动化与计算机技术—控制科学与工程]