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 (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.

研究表明，在不久的将来，使用脉冲星定时阵列（PTA）是最有可能检测超低频重力波的方法之一。尽管尚未报道PTA捕获重力波（GWs），但已报道了许多相关的理论研究和一些有意义的检测限。在这项研究中，我们集中于单个超大质量二元黑洞的纳赫GW。给定特定的脉冲星（PSR J1909-3744，PSR J1713 + 0747，PSR J0437-4715），可以模拟具有高斯白噪声的PTA中相应的GW诱导的时序残差。此外，我们使用基于神经网络的机器学习报告了模拟PTA数据的分类和潜在GW源的参数估计。作为分类器，当我们的模拟数据的组合信噪比≥1.33时，卷积神经网络显示出高精度。此外，我们应用了递归神经网络来估算光源的chi质量（ℳ）和光度距离（脉冲星和贝叶斯神经网络（BNN）的D _p），以获得chi质量估计的不确定性。不确定性的知识对于天体观测至关重要。在我们的案例中，线性调频质量估计的平均相对误差小于13.6％。尽管这些结果是通过模拟的PTA数据获得的，但我们认为它们对于实现PTA数据分析中的智能处理将非常重要。