  Searches for gravitational-wave bursts (transient signals, typically of
unknown waveform) require identification of weak signals in background detector
noise. The sensitivity of such searches is often critically limited by
non-Gaussian noise fluctuations which are difficult to distinguish from real
signals, posing a key problem for transient gravitational-wave astronomy.
Current noise rejection tests are based on the analysis of a relatively small
number of measured properties of the candidate signal, typically correlations
between detectors. Multivariate analysis (MVA) techniques probe the full space
of measured properties of events in an attempt to maximise the power to
accurately classify events as signal or background. This is done by taking
samples of known background events and (simulated) signal events to train the
MVA classifier, which can then be applied to classify events of unknown type.
We apply the boosted decision tree (BDT) MVA technique to the problem of
detecting gravitational-wave bursts associated with gamma-ray bursts. We find
that BDTs are able to increase the sensitive distance reach of the search by as
much as 50%, corresponding to a factor of ~3 increase in sensitive volume. This
improvement is robust against trigger sky position, large sky localisation
error, poor data quality, and the simulated signal waveforms that are used.
Critically, we find that the BDT analysis is able to detect signals that have
different morphologies to those used in the classifier training and that this
improvement extends to false alarm probabilities beyond the 3{\sigma}
significance level. These findings indicate that MVA techniques may be used for
the robust detection of gravitational-wave bursts with a priori unknown
waveform.
