Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years.Most existing methods focus on the data obtained by a single detector.However,the signal-to-noise ratio(SNR)of gravitational wave signals in a single detector is pretty low,making it hard for deep neural networks to learn effective features.Therefore,how to use the observation signals obtained by multiple detectors in deep learning methods is a serious issue.We simulate binary neutron star signals from multiple detectors,including the Advanced LIGO and Virgo detectors.We calculate coherent SNR of multiple detectors using a fully coherent all-sky search method and obtain the coherent SNR data required for our proposed deep learning method.Inspired by the principle of attention network Squeeze-and-Excitation Networks(SENet)and the soft thresholding shrinkage function,we propose a novel Squeeze-and-Excitation Shrinkage(SES)module to better extract effective features.Then we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network(GW-SESNet)detection model.We train and validate the performance of our model on the coherent SNR data set.Our model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.

Rui-Qing Yan;Wei Liu;Zong-Yao Yin;Rong Ma;Si-Ying Chen;Dan Hu;Dan Wu;Xian-Chuan Yu

School of Artificial Intelligence,Beijing Normal University,Beijing 100875,China;National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Department of Radiology and BRIC,University of North Carolina at Chapel Hill,Chapel Hill,NC 27599 United States of America;National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China

天文和天体物理学研究

[1]Rui-Qing Yan;Wei Liu;Zong-Yao Yin;Rong Ma;Si-Ying Chen;Dan Hu;Dan Wu;Xian-Chuan Yu-.Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR)[J].天文和天体物理学研究,2022(11):86-98

SESNet,excellently

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