  Based on the prior O1-O2 observing runs, about 30% of the data collected by
Advanced LIGO and Virgo in the next observing runs are expected to be
single-interferometer data, i.e., they will be collected at times when only one
detector in the network is operating in observing mode. Searches for
gravitational wave signals from supernova events do not rely on matched
filtering techniques because of the stochastic nature of the signals. If a
Galactic supernova occurs during single-interferometer times, separation of its
unmodelled gravitational-wave signal from noise will be even more difficult due
to lack of coherence between detectors. We present a novel machine learning
method to perform single-interferometer supernova searches based on the
standard LIGO-Virgo coherentWave-Burst pipeline. We show that the method may be
used to discriminate Galactic gravitational-wave supernova signals from noise
transients, decrease the false alarm rate of the search, and improve the
supernova detection reach of the detectors.
