  We demonstrate the use of a convolutional neural network to perform
neutron-gamma pulse shape discrimination, where the only inputs to the network
are the raw digitised SiPM signals from a dual scintillator detector element
made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset
was created to train the network by exposing the detector to an AmBe source,
and a data-driven method utilsing a separate PMT was used to assign labels to
the recorded signals. This approach is compared to the charge integration and
continuous wavelet transform methods and is found to provide superior levels of
discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural
network is capable of extracting interpretable features directly from the raw
data. In addition, by visualising the high-dimensional representations of the
network with the t-SNE algorithm, we discover that not only is this method
robust to minor mislabeling of the training dataset but that it is possible to
identify an underlying substructure within the signals that goes beyond the
original labelling. This technique could be utilised to explore and cluster
complex, raw detector data in a novel way that may reveal more insights than
standard analysis methods.
