Deep learning merger masses estimation from gravitational waves signals in the frequency domain

Understand

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).