首页|Gravitational wave search by time-scale-recursive denoising and matched filtering
Gravitational wave search by time-scale-recursive denoising and matched filtering
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Gravitational wave search by time-scale-recursive denoising and matched filtering
In our previous work[Physical Review D,2024,109(4):043009],we introduced MSNRnet,a framework integrating deep learn-ing 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 dif-ferent 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 denois-ing 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.
gravitational wavematched filteringdeep learning
Cunliang Ma、Chenyang Ma、Zhoujian Cao、Mingzhen Jia
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School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China
Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control,Ganzhou 341000,China
Institute for Frontiers in Astronomy and Astrophysics,Beijing Normal University,Beijing 102206,China
School of Fundamental Physics and Mathematical Sciences,Hangzhou Institute for Advanced Study,UCAS,Hangzhou 310024,China
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gravitational wave matched filtering deep learning
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