one which had been smeared downwards, and one which had not been smeared at all. This smearing corresponded to the extremities of a Gaussian envelope, and was applied to jets, leptons and the missing energy in each event accordingly. Furthermore, we also had a set of signal events which had not been smeared. The smearing had the effect of shifting the kinematic features of the background such that the events which had been smeared upwards looked more like signal events, and the ones which had been smeared downwards looked less like signal events. This can be seen from the distributions in Fig. \[fig:observables\].

We then trained an autoencoder on all the background events for the purpose of using it to detect signal events, which have a higher expected reconstruction loss. In Fig. \[fig:AEROC\], the ROC curves are the result of testing the classification performance of the autoencoder for the signal separately against each background, and as expected, the autoencoder had a harder time discriminating the signal events against background events which had been smeared upwards. We then combined this with an adversarial neural network, which had the objective of recognising which direction each background sample had been smeared in based 