Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR

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

作者机构：School of Artificial IntelligenceBeijing Normal UniversityBeijing 100875China National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC 27599 United States of America National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2022年第22卷第11期

页      面：84-96页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Foundation of China and Beijing Natural Science Foundation(No.4224091) China Postdoctoral Science Foundation(No.2021M693402) 

主　　题：methods:data analysis methods:statistical gravitational waves 

摘      要：Deep learning techniques have been applied to the detection of gravitational wave signals in the past few *** existing methods focus on the data obtained by a single ***,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 ***,how to use the observation signals obtained by multiple detectors in deep learning methods is a serious *** simulate binary neutron star signals from multiple detectors,including the Advanced LIGO and Virgo *** calculate coherent SNR of multiple detectors using a fully coherent allsky search method and obtain the coherent SNR data required for our proposed deep learning *** 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 *** we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network(GWSESNet)detection *** train and validate the performance of our model on the coherent SNR data *** model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.