We provide a comprehensive multi-aspect study on the performance of a pipeline used by the LIGO-Virgo Collaboration for estimating parameters of gravitational-wave bursts .
We add simulated signals with four different morphologies ( sine-Gaussians , Gaussians , white-noise bursts , and binary black hole signals ) to simulated noise samples representing noise of the two Advanced LIGO detectors during their first observing run .
We recover them with the BayesWave ( BW ) pipeline to study its accuracy in sky localization , waveform reconstruction , and estimation of model-independent waveform parameters .
BW localizes sources with a level of accuracy comparable for all four morphologies , with the median separation of actual and estimated sky locations ranging from 25.1 ^ { \circ } to 30.3 ^ { \circ } .
This is a reasonable accuracy in the two-detector case , and is comparable to accuracies of other localization methods studied previously .
As BW reconstructs generic transient signals with sine-Gaussian wavelets , it is unsurprising that BW performs the best in reconstructing sine-Gaussian and Gaussian waveforms .
BW ’ s accuracy in waveform reconstruction increases steeply with network signal-to-noise ratio ( SNR _ { net } ) , reaching a 85 \% and 95 \% match between the reconstructed and actual waveform below SNR _ { net } \approx 20 and SNR _ { net } \approx 50 , respectively , for all morphologies .
BW ’ s accuracy in estimating central moments of waveforms is only limited by statistical errors in the frequency domain , and is affected by systematic errors too in the time domain as BW can not reconstruct low-amplitude parts of signals overwhelmed by noise .
The figures of merit we introduce can be used in future characterizations of parameter estimation pipelines .