Ricerc@Sapienza

Gravitational waves (GWs) are ripples in the spacetime emitted by sources such as black holes (BHs) and neutron stars (NSs). The detection of GW by the Laser Interferometer Gravitational Observatory (LIGO) not only has proven the existence of these waves but have also opened the era of gravitational wave astronomy. In fact, many astrophysical properties of the emitting sources can be extracted from the detected signals, such as the mass and the spin of the system, moreover constraints on the graviton mass can be set and more generally, studies of fundamental physics in strongly curved space-time can be done. All these kinds of study have been done on the three GW detections from binary BHs merger that the LIGO and Virgo Collaborations have achieved since September 2015, day of the first detection. Many other types of GWs sources are expected to exist. Among these, there are continuous gravitational waves signals (CWs), characterized by a semi-periodic waveform, with duration much longer than the usual span of experimental Runs (~1 yr). This type of signals have the unique properties to be self-coherent, allowing in the case that the phase evolution is known, the application of matched filter techniques in order to detect them and estimate their physical parameters with very high precision. CW signals cover a wide region in GW astronomy. In ground based interferometers CWs are expected from asymmetric spinning NSs while in the future spacecraft GW detectors (e.g. LISA), we expect to find these signals emitted by a large population of coalescing BHs. Matched filter techniques are highly computational expensive and the analysis is not possible if the source parameters are not accurately known, due to the presence of too many templates. For these reasons efficient algorithms in high dimensional spaces, such as machine learning and Bayesian Markov chains, are often employed. The aim of this project is set the basics to develop a new pipeline for CW detection.