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Improved deep learning techniques in gravitational-wave data analysis
Xia, Heming1; Shao, Lijing2,3; Zhao, Junjie4,5; Cao, Zhoujian6
2021-01-21
Source PublicationPHYSICAL REVIEW D
ISSN2470-0010
Volume103Issue:2Pages:14
AbstractIn recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has significant advantages in efficiency in GW signal detection tasks. In addition, matched-filtering techniques are based on the template bank of the existing theoretical waveform, which makes it difficult to find GW signals beyond theoretical expectation. In this paper, based on the task of GW detection of binary black holes, we introduce the optimization techniques of deep learning, such as batch normalization and dropout, to CNN models. Detailed studies of model performance are carried out. Through this study, we recommend to use batch normalization and dropout techniques in CNN models in GW signal detection tasks. Furthermore, we investigate the generalization ability of CNN models on different parameter ranges of GW signals. We point out that CNN models are robust to the variation of the parameter range of the GW waveform. This is a major advantage of deep learning models over matched-filtering techniques.
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
DOI10.1103/PhysRevD.103.024040
WOS KeywordNEURAL-NETWORKS
Language英语
Funding ProjectNational Natural Science Foundation of China[11975027] ; National Natural Science Foundation of China[11991053] ; National Natural Science Foundation of China[11690023] ; National Natural Science Foundation of China[11721303] ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology[2018QNRC001] ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB23010200]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Max Planck Partner Group Program - Max Planck Society ; Max Planck Partner Group Program - Max Planck Society ; High-Performance Computing Platform of Peking University ; High-Performance Computing Platform of Peking University ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaAstronomy & Astrophysics ; Physics
WOS SubjectAstronomy & Astrophysics ; Physics, Particles & Fields
WOS IDWOS:000609262900006
PublisherAMER PHYSICAL SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/79781
Collection中国科学院国家天文台
Corresponding AuthorShao, Lijing
Affiliation1.Peking Univ, Sch Phys, Dept Astron, Beijing 100871, Peoples R China
2.Peking Univ, Kavli Inst Astron & Astrophys, Beijing 100871, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
4.Peking Univ, Sch Phys, Beijing 100871, Peoples R China
5.Peking Univ, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China
6.Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Xia, Heming,Shao, Lijing,Zhao, Junjie,et al. Improved deep learning techniques in gravitational-wave data analysis[J]. PHYSICAL REVIEW D,2021,103(2):14.
APA Xia, Heming,Shao, Lijing,Zhao, Junjie,&Cao, Zhoujian.(2021).Improved deep learning techniques in gravitational-wave data analysis.PHYSICAL REVIEW D,103(2),14.
MLA Xia, Heming,et al."Improved deep learning techniques in gravitational-wave data analysis".PHYSICAL REVIEW D 103.2(2021):14.
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