Since their prediction in Einstein’s theory of general relativity, gravitational waves took a hundred years to be actually detected. Nevertheless, the wait was well rewarded since measuring gravitational waves did not only provide a strong confirmation of the predictions of general relativity, but also offered a new way to study fundamental interactions. The information extracted from gravitational-wave signals allows us to probe the theory of general relativity in the strong-field regime, as well as to study the equation of state describing the extremely dense matter inside neutron stars.
In this thesis, we investigated both these aspects. In particular, we developed a method to probe general relativity by looking at the amplitude of subdominant modes. On the other hand, we constructed a model that describes the gravitational-wave signal emitted during the full coalescence of a binary neutron star system, including the postmerger, and we employed it in parameter estimation analyses. We also turned our attention to next-generation detectors, which are expected to increase sensitivity and to extend the detection bandwidth both at lower and higher frequencies with respect to current detectors. We studied how this will improve our postmerger analysis, but also the measurements of the system’s parameters from the inspiral part of the signal only. In particular, for the latter, we investigated how the different designs proposed for the Einstein Telescope will affect such measurements.
The high sensitivity of future detectors, however, comes also with downsides: the computational cost of the analyses will increase, thus it is necessary to find alternative techniques in order to make the analyses feasible; furthermore, the high precision with which we will be able to measure the sources’ parameters will enhance the effect of systematic errors, induced, for example, by the waveform models employed to analyze the data. For this reason, we analyzed some of the gravitational-wave events in the latest catalog with different waveform models, in order to identify possible preferences among the models and, consequently, highlight potential systematics.