We report the results of an in-depth analysis of the parameter estimation
capabilities of BayesWave, an algorithm for the reconstruction of
gravitational-wave signals without reference to a specific signal model. Using
binary black hole signals, we compare BayesWave's performance to the
theoretical best achievable performance in three key areas: sky localisation
accuracy, signal/noise discrimination, and waveform reconstruction accuracy.
BayesWave is most effective for signals that have very compact time-frequency
representations. For binaries, where the signal time-frequency volume decreases
with mass, we find that BayesWave's performance reaches or approaches
theoretical optimal limits for system masses above approximately 50 M_sun. For
such systems BayesWave 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 follow analytical
predictions, and that only a small fraction of signals are discarded as
glitches at a false alarm rate of 1/100 y. Finally, the match between
BayesWave- reconstructed signals and injected signals is broadly consistent
with first-principles estimates of the maximum possible accuracy, peaking at
about 0.95 for high mass systems and decreasing for lower-mass systems. These
results demonstrate the potential of unmodelled signal reconstruction
techniques for gravitational-wave astronomy.Comment: 10 pages, 7 figure
