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Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR | |
Yan, Rui-Qing1; Liu, Wei2; Yin, Zong-Yao1; Ma, Rong1; Chen, Si-Ying1; Hu, Dan3,4; Wu, Dan5; Yu, Xian-Chuan1 | |
发表期刊 | RESEARCH IN ASTRONOMY AND ASTROPHYSICS |
ISSN | 1674-4527 |
2022-11-01 | |
卷号 | 22期号:11页码:13 |
通讯作者 | Yan, Rui-Qing(yanruiqing@mail.bnu.edu.cn) |
摘要 | 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. |
关键词 | methods: data analysis methods: statistical gravitational waves |
DOI | 10.1088/1674-4527/ac846c |
关键词[WOS] | CLASSIFICATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China ; Beijing Natural Science Foundation[4224091] ; China Postdoctoral Science Foundation[2021M693402] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; China Postdoctoral Science Foundation |
WOS研究方向 | Astronomy & Astrophysics |
WOS类目 | Astronomy & Astrophysics |
WOS记录号 | WOS:000870859600001 |
出版者 | NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50276 |
专题 | 多模态人工智能系统全国重点实验室_智能机器人系统研究 |
通讯作者 | Yan, Rui-Qing |
作者单位 | 1.Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA 4.Univ N Carolina, BRIC, Chapel Hill, NC 27599 USA 5.Chinese Acad Sci, Natl Astron Observ, Beijing 100101, Peoples R China |
推荐引用方式 GB/T 7714 | Yan, Rui-Qing,Liu, Wei,Yin, Zong-Yao,et al. Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2022,22(11):13. |
APA | Yan, Rui-Qing.,Liu, Wei.,Yin, Zong-Yao.,Ma, Rong.,Chen, Si-Ying.,...&Yu, Xian-Chuan.(2022).Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,22(11),13. |
MLA | Yan, Rui-Qing,et al."Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 22.11(2022):13. |
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