Neural network-based search for unmodeled transients in LIGO-Virgo-KAGRA’s third observing run

This paper presents the results of a neural network (NN)-based search for short-duration gravitationalwave transients in data from the third observing run of LIGO, Virgo, and KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30–1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the gravitational wave anomalous knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, and a range of detector glitches are identified. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study explores the potential of GWAK to generalize gravitational-wave searches and complement existing pipelines and demonstrates sensitivity to several classes of simulated short-duration GW transients, including corecollapse supernovae and other modeled sources, which have not yet been observed.