Convolutional neural network regression to estimate the mass parameter of astrophysical binary black hole systems

In this paper we propose the use of a deep learning based model for inferring astrophysical information of binary black hole (BBH) systems from observed gravitational wave (GW) signals. We focused in estimating the total mass of BBH systems $M_{total}$ using a convolutional neural network regression (CNNR) model. We built a large dataset of 2D images representing the time-frequency evolution of BBH GW signals which are embedded in noise, where for each generated image the real total mass is known. $M_{total} \in [10, 200] M_\odot$. A hold-out cross-validation procedure was performed to train and evaluate five architectures of CNNR models with different number and sizes of kernels. The results indicate that the proposed deep neural network models for regression provide reliable point-parameter estimations with high accuracy. This estimation parameter approach can be easily extended to reconstruct more parameters from astrophysical sources directly from obseved GW events.