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We demonstrate how a morphological veto involving Bayesian statistics canimprove the receiver-operating characteristic (ROC) curves of the currentsearch for core-collapse supernovae (CCSNe) as implemented by the coherentWaveburst (cWB) algorithm. Examples involving two implementations of BayesWave(BW), one that makes no assumption of the polarization state of thegravitational wave (GW) and one that uses the same elliptical polarizationsettings adopted in previous usages for Binary systems are provided on the setof waveforms currently adopted for the first and second Advanced LIGO (aLIGO)science runs for the targeted CCSNe search. A comparison of the performance forall-sky searches versus the targeted searches with optical triggers isprovided. The average cWB+BW ROC range improvements with respect to a fixedfalse-alarm rate (FAR) for slowly-rotating waveforms ranged from [+1.30%,+15.76%] while the improvement for rapidly-rotating waveforms were on the orderof [+1.19%, +22.05%]. The application of BW to CCSN GW triggers also shows asignificant reduction of the FAR while maintaining detection efficiency andremaining sensitive to a wide range of morphological CCSNe signals. It appearsthat the code developed for arbitrarily polarized signals outperforms theprevious code for the GW morphologies tested.

Enhancing the Sensitivity of Searches for Gravitational Waves from Core-Collapse Supernovae with a Bayesian classification of candidate events