Deep learning for parameter estimation of supermassive binary black holes with simulated LISA data

Abstract

Previous studies have demonstrated that deep learning is an effective approach for processing gravitational wave (GW) data obtained from ground-based detectors, as it can enhance the efficiency of data processing and offer great potential for real-time parameter estimation. In this paper, we explore three different deep learning architectures (MCD, TFP, and VI) for inferring the chirp mass (Mc) and the luminosity distance (DL) of supermassive binary black holes (SMBBHs) using simulated data from the Laser Interferometer Space Antenna (LISA). We train the neural networks with the simulated data and evaluate their performance on predicting the parameters (Mc,DL). The results show that more than 97.5% of the true values fall within the 3σ confidence interval of the predicted values with the optimal network. To verify the accuracy of the network on parameter estimation, we also calculate the estimation error of the parameters using the Fisher Information Matrix (FIM) with the same simulated data. By comparing the root-mean-square error (RMSE) between deep learning and FIM, we find that the two methods are comparable, which implies that deep learning can be reliable for GW parameter estimation.