Abstract:
Since their first detection by the Advanced LIGO interferometers, GW150914, gravitational waves have provided unique scientific insight into high-energy astrophysical events, such as compact binary mergers involving black holes and neutron stars. Core-collapse supernovae are among the types of signals that have eluded detection thus far, and a significant effort is therefore being put into achieving a detection in coming years. Compared to binary mergers, core-collapse supernovae cannot be modelled precisely, and it is, therefore, impossible to apply classical matched filter techniques for detection and analysis. In order to gain valuable information from this type of event, a correct estimate of the source parameters has to be obtained, such as the mass and radius. Parameter estimation heavily relies on a critical aspect of gravitational wave data analysis: signal reconstruction. In this thesis project, we efficiently detected and reconstructed gravitational wave signals embedded in the typical noise background of the Einstein Telescope. Specifically, we focused on the use of wavelet-based algorithms, which have been successfully applied for signal reconstruction in many fields. We first detected and clustered the triggers from simulated core-collapse supernovae gravitational wave events and then reconstructed the signals. For the background, we used simulated Gaussian noise built from the estimated sensitivity curve of the Einstein Telescope. We employed gravitational waveforms obtained from modern 3D simulations to model core-collapse supernovae signals.
