ROC curves (b) for a classifier trained to classify signal and background events. The three background distributions result from the three different directions of smearing.[]{data-label="fig:cls"}](figures/CLS3response.pdf "fig:"){width="\textwidth"}

[0.49]{} ![Supervised neural network classifier output (a) and ROC curves (b) for a classifier trained to classify signal and background events. The three background distributions result from the three different directions of smearing.[]{data-label="fig:cls"}](figures/CLS3ROC.pdf "fig:"){width="\textwidth"}

The network consists of two hidden layers each with 20 nodes, with ReLu activations, and a final layer with a single sigmoid output. We use a binary cross entropy loss function since there are two possible classes. A class weighting in the loss function is used to account for the higher frequency of background events in the training data, i.e. the loss of the signal events are weighted higher. The network is trained using the Adam optimiser [@Kingma:2014] with a learning rate of 0.01 and a batch size of 500 for 500 epochs. The network is implemented in <span style="font-variant:small-caps;">Keras</span> [@keras] with a <span style="font-variant:small-caps;">TensorFlow</span> [@tensorflow] backend, and we use these throughout the rest of this paper. The results are shown by the distributions of the classifier outputs and the corresponding receiver operating characteristic (ROC) curves in Fig. \[fig:cls\]. These 