  With the advanced LIGO and Virgo detectors taking observations the detection
of gravitational waves is expected within the next few years. Extracting
astrophysical information from gravitational wave detections is a well-posed
problem and thoroughly studied when detailed models for the waveforms are
available. However, one motivation for the field of gravitational wave
astronomy is the potential for new discoveries. Recognizing and characterizing
unanticipated signals requires data analysis techniques which do not depend on
theoretical predictions for the gravitational waveform. Past searches for
short-duration un-modeled gravitational wave signals have been hampered by
transient noise artifacts, or "glitches," in the detectors. In some cases, even
high signal-to-noise simulated astrophysical signals have proven difficult to
distinguish from glitches, so that essentially any plausible signal could be
detected with at most 2-3 $\sigma$ level confidence. We have put forth the
BayesWave algorithm to differentiate between generic gravitational wave
transients and glitches, and to provide robust waveform reconstruction and
characterization of the astrophysical signals. Here we study BayesWave's
capabilities for rejecting glitches while assigning high confidence to
detection candidates through analytic approximations to the Bayesian evidence.
Analytic results are tested with numerical experiments by adding simulated
gravitational wave transient signals to LIGO data collected between 2009 and
2010 and found to be in good agreement.
