  Coherent WaveBurst is a generic, multidetector gravitational wave burst
search based on the excess power approach. The coherent WaveBurst algorithm
currently employed in the all-sky short-duration gravitational wave burst
search uses a conditional approach on selected attributes in the
multidimensional event attribute space to distinguish between noisy events from
that of astrophysical origin. We have been developing a supervised machine
learning approach based on the Gaussian mixture modeling to model the attribute
space for signals as well as noise events to enhance the probability of burst
detection [Gayathri et al.Phys. Rev. D 102, 104023 (2020)]. We further extend
the GMM approach to the all-sky short-duration coherent WaveBurst search as a
postprocessing step on events from the first half of the third observing run
(O3a). We show an improvement in sensitivity to generic gravitational wave
burst signal morphologies as well as the astrophysical source such as
core-collapse supernova models due to the application of our Gaussian mixture
model approach to coherent WaveBurst triggers. The Gaussian mixture model
method recovers the gravitational wave signals from massive compact binary
coalescences identified by coherent WaveBurst targeted for binary black holes
in GWTC-2, with better significance than the all-sky coherent WaveBurst search.
No additional significant gravitational wave bursts are observed.
