  The determination of the physical parameters of gravitational wave events is
a fundamental pillar in the analysis of the signals observed by the current
ground-based interferometers. Typically, this is done using Bayesian inference
approaches which, albeit very accurate, are very computationally expensive. We
propose a convolutional neural network approach to perform this task. The
convolutional neural network is trained using simulated signals injected in a
Gaussian noise. We verify the correctness of the neural network's output
distribution and compare its estimates with the posterior distributions
obtained from traditional Bayesian inference methods for some real events. The
results demonstrate the convolutional neural network's ability to produce
posterior distributions that are compatible with the traditional methods.
Moreover, it achieves a remarkable inference speed, lowering by orders of
magnitude the times of Bayesian inference methods, enabling real-time analysis
of gravitational wave signals. Despite the observed reduced accuracy in the
parameters, the neural network provides valuable initial indications of key
parameters of the event such as the sky location, facilitating a
multi-messenger approach.
