Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array
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December 2020 Vol. 63 No. 12: 129511 https://doi.org/10.1007/s11433-020-1609-y
Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array MengNi Chen1, YuanHong Zhong2* , Yi Feng3,4,5, Di Li5,6, and Jin Li1* 1 College of Physics, Chongqing University, Chongqing 401331, China; of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China; 3 National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China; 4 University of Chinese Academy of Sciences, Beijing 100049, China; 5 CAS Key Laboratory of FAST, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China; 6 NAOC-UKZN Computational Astrophysics Centre, University of KwaZulu-Natal, Durban 4000, South Africa 2 School
Received July 1, 2020; accepted August 10, 2020; published online October 20, 2020
Studies 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 (Dp ) 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. machine learning, neural network, PTA, GW-induced time residuals PACS number(s): Citation:
04.30.Db, 07.05.Mh, 04.80.Nn, 04.70.Bw
M. N. Chen, Y. H. Zhong, Y. Feng, D. Li, and J. Li, Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array, Sci. China-Phys. Mech. Astron. 63, 129511 (2020), https://doi.org/10.1007/s11433-020-1609-y
1 Introduction The use of pulsar timing arrays (PTAs) to detect nanohertz gravitational waves (GWs) is being considered as a supplement to the direct detection of GWs; this approach based on PTAs is considered a major milestone in GW astrophysics following the detection of GWs in the Laser Interferom
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