Dedicated Core Collapse Supernovae search using the ML-enhanced Coherent WaveBurst search algorithm

Core Collapse supernovae (CCSN) are among the most powerful and interesting events within our universe. Along with electromagnetic and neutrino emissions, they are believed to produce a detectable gravitational wave (GW) signal. We are interested in detecting these multimessenger events in the hopes of being able to identify the morphology, and consequently the mechanism, of CCSN. The exact mechanism of CCSN is not known and so it is not known whether template searches will be able to identify these events effectively. In order to detect these GW signals without the use of templates, the Coherent WaveBurst (cWB) search algorithm is used to isolate coherent power in the data from the network of LIGO-Virgo GW detectors using the high-resolution Wavescan Time-Frequency (TF) mapping. cWB then uses a modified XGB based machine learning (ML) method to further distinguish real signal from noise. Here we present the working algorithm of the ML-enhanced cWB search for the use of CCSN detection. We also report the results of CCSN detection with cWB which was tested on the third observing run (O3) data of the LIGO-Virgo detectors.