Gravitational-wave burst searches enhanced with Machine Learning

The wealth of gravitational-wave (GW) discoveries by LIGO-Virgo-KAGRA provides new insight into the Universe. All of them come from compact binary systems, and a non-binary GW source is still awaiting discovery. Core-collapse supernovae are prime examples but also cosmic strings, pulsar glitches, and many others. The LIGO-Virgo-KAGRA were performing many searches for these generic sources, and coherent WaveBurst (cWB) is one of the algorithms used to identify short-duration transients. It was the only pipeline providing public alerts during LIGO-Virgo-KAGRA third observing run for unmodeled transients. The sensitivity of cWB was recently enhanced with Machine Learning. In my presentation, I will show that the new cWB version is more sensitive to a wide range of GW morphologies compared to the standard version. I will present results from the generic search with the cWB-enhanced algorithm.