  We introduce the use of conditional generative adversarial networks
forgeneralised gravitational wave burst generation in the time
domain.Generativeadversarial networks are generative machine learning models
that produce new databased on the features of the training data set. We
condition the network on fiveclasses of time-series signals that are often used
to characterise gravitational waveburst searches: sine-Gaussian, ringdown,
white noise burst, Gaussian pulse and binaryblack hole merger. We show that the
model can replicate the features of these standardsignal classes and, in
addition, produce generalised burst signals through interpolationand class
mixing. We also present an example application where a convolutional
neuralnetwork classifier is trained on burst signals generated by our
conditional generativeadversarial network. We show that a convolutional neural
network classifier trainedonly on the standard five signal classes has a poorer
detection efficiency than aconvolutional neural network classifier trained on a
population of generalised burstsignals drawn from the combined signal class
space.
