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We demonstrate the use of a convolutional neural network to performneutron-gamma pulse shape discrimination, where the only inputs to the networkare the raw digitised SiPM signals from a dual scintillator detector elementmade of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled datasetwas 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 tothe recorded signals. This approach is compared to the charge integration andcontinuous wavelet transform methods and is found to provide superior levels ofdiscrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neuralnetwork is capable of extracting interpretable features directly from the rawdata. In addition, by visualising the high-dimensional representations of thenetwork with the t-SNE algorithm, we discover that not only is this methodrobust to minor mislabeling of the training dataset but that it is possible toidentify an underlying substructure within the signals that goes beyond theoriginal labelling. This technique could be utilised to explore and clustercomplex, raw detector data in a novel way that may reveal more insights thanstandard analysis methods.

Pulse Shape Discrimination and Exploration of Scintillation Signals Using Convolutional Neural Networks