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Machine learning for nanohertz gravitational wave detection and parameter estimation with pulsar timing array

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Abstract

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.

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Author information

Authors and Affiliations

  1. College of Physics, Chongqing University, Chongqing, 401331, China

    MengNi Chen & Jin Li

  2. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China

    YuanHong Zhong

  3. National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100012, China

    Yi Feng

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yi Feng

  5. CAS Key Laboratory of FAST, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, China

    Yi Feng & Di Li

  6. NAOC-UKZNComputational Astrophysics Centre, University of KwaZulu-Natal, Durban, 4000, South Africa

    Di Li

Authors
  1. MengNi Chen
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  2. YuanHong Zhong
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  3. Yi Feng
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  4. Di Li
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  5. Jin Li
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Corresponding authors

Correspondence to YuanHong Zhong or Jin Li.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11873001, 11725313, and 11690024), the Natural Science Foundation of Chongqing (Grant No. cstc2018jcyjAX0767), the National Key Research and Development Program of China (Grant No. 2017YFA0402600), the CAS International Partnership Program (Grant No. 114A11KYSB20160008), and the CAS Strategic Priority Research Program (Grant No. XDB23000000).

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Chen, M., Zhong, Y., Feng, Y. et al. 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

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  • DOI: https://doi.org/10.1007/s11433-020-1609-y

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