NAOC Open IR
Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array
Chen, MengNi1; Zhong, YuanHong2; Feng, Yi3,4,5; Li, Di5,6; Li, Jin1
2020-10-20
Source PublicationSCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
ISSN1674-7348
Volume63Issue:12Pages:10
AbstractStudies have shown that the use of pulsar timing arrays (PTAs) is among the approaches with the highest potential to detect very low-frequency gravitational waves in the near future. Although the capture of gravitational waves (GWs) by PTAs has not been reported yet, many related theoretical studies and some meaningful detection limits have been reported. In this study, we focused on the nanohertz GWs from individual supermassive binary black holes. Given specific pulsars (PSR J1909-3744, PSR J1713+0747, PSR J0437-4715), the corresponding GW-induced timing residuals in PTAs with Gaussian white noise can be simulated. Further, we report the classification of the simulated PTA data and parameter estimation for potential GW sources using machine learning based on neural networks. As a classifier, the convolutional neural network shows high accuracy when the combined signal to noise ratio >= 1.33 for our simulated data. Further, we applied a recurrent neural network to estimate the chirp mass (M) of the source and luminosity distance (D-p) of the pulsars and Bayesian neural networks (BNNs) to obtain the uncertainties of chirp mass estimation. Knowledge of the uncertainties is crucial to astrophysical observation. In our case, the mean relative error of chirp mass estimation is less than 13.6%. Although these results are achieved for simulated PTA data, we believe that they will be important for realizing intelligent processing in PTA data analysis.
Keywordmachine learning neural network PTA GW-induced time residuals
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program
DOI10.1007/s11433-020-1609-y
Language英语
Funding ProjectNational Natural Science Foundation of China[11873001] ; National Natural Science Foundation of China[11725313] ; National Natural Science Foundation of China[11690024] ; Natural Science Foundation of Chongqing[cstc2018jcyjAX0767] ; National Key Research and Development Program of China[2017YFA0402600] ; CAS International Partnership Program[114A11KYSB20160008] ; CAS Strategic Priority Research Program[XDB23000000]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing ; Natural Science Foundation of Chongqing ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; CAS International Partnership Program ; CAS International Partnership Program ; CAS Strategic Priority Research Program ; CAS Strategic Priority Research Program
WOS Research AreaPhysics
WOS SubjectPhysics, Multidisciplinary
WOS IDWOS:000584404800001
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/80973
Collection中国科学院国家天文台
Corresponding AuthorZhong, YuanHong; Li, Jin
Affiliation1.Chongqing Univ, Coll Phys, Chongqing 401331, Peoples R China
2.Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Natl Astron Observ, CAS Key Lab Fast, Beijing 100101, Peoples R China
6.Univ KwaZulu Natal, NAOC UKZN Computat Astrophys Ctr, ZA-4000 Durban, South Africa
Recommended Citation
GB/T 7714
Chen, MengNi,Zhong, YuanHong,Feng, Yi,et al. Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array[J]. SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,2020,63(12):10.
APA Chen, MengNi,Zhong, YuanHong,Feng, Yi,Li, Di,&Li, Jin.(2020).Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array.SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,63(12),10.
MLA Chen, MengNi,et al."Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array".SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY 63.12(2020):10.
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