Bayesian inference analysis of unmodelled gravitational-wave transients

We report the results of an in-depth analysis of the parameter estimation capabilities of , an algorithm for the reconstruction of gravitational-wave signals without reference to a specific signal model. Using binary black hole signals, we compare 's performance to the theoretical best achievable performance in three key areas: sky localisation accuracy, signal/noise discrimination, and waveform reconstruction accuracy. is most effective for signals that have very compact time-frequency representations. For binaries, where the signal time-frequency volume decreases as the system mass increases, we find that 's performance reaches or approaches theoretical optimal limits for system masses above approximately 50 . For such systems is able to localise the source on the sky as well as templated Bayesian analyses that rely on a precise signal model, and it is better than timing-only triangulation in all cases. We also show that the discrimination of signals against glitches and noise closely follows analytical predictions, and that only a small fraction of signals are discarded as glitches at a false alarm rate of 1/100 yr. Finally, the match between -reconstructed signals and injected signals is broadly consistent with first-principles estimates of the maximum possible accuracy, peaking at about for high mass systems and decreasing for lower-mass systems. These results demonstrate the potential of unmodelled signal reconstruction techniques for gravitational-wave astronomy.