  Detection of gravitational waves (GW) from compact binary mergers provide a
new window into multi-messenger astrophysics. The standard technique to
determine the merger parameters is matched filtering, consisting in comparing
the signal to a template bank. This approach can be time consuming and
computationally expensive due to the large amount of experimental data which
needs to be analyzed.
  In the attempt to find more efficient data analysis methods we develop a new
frequency domain convolutional neural network (FCNN) to predict the merger
masses from the spectrogram of the detector signal, and compare it to time
domain neural networks (TCNN). Since FCNNs are trained using spectrograms, the
dimension of the input is reduced as compared to TCNNs, implying a
substantially lower number of model parameters, and consequently less
over-fitting. The additional time required to compute the spectrogram is
approximately compensated by the lower execution time of the FCNNs, due to the
lower number of parameters. In our analysis FCNNs show a slightly better
performance on validation data and a substantially lower over-fit, as expected
due to the lower number of parameters, providing a new promising approach to
the analysis of GW detectors data, which could be further improved in the
future by using more efficient and faster computations of the spectrogram.
