all events below the cut as background events, and then moving this threshold across all values. This is similar to how the ROC curves are calculated from the output of the classifier, where the threshold is varied between 0 and 1 instead.

As we saw for the classifier, the smearing of the background has an effect on how well the autoencoder can be used to classify events, with the events which have been smeared upwards being mislabelled as signal events more often. It is important to note that the overall classification performance of the autoencoder is much worse than for the dedicated supervised classifier in Sec. \[sec:supervised\]. However, this is not surprising—the autoencoder is only ever trained on background events, and only sees the signal events during testing. Thus, for the separation between signal and background it learns the intricate kinematic features of the background only. Furthermore, the optimisation objective of the classifier is for it to achieve a strong classification performance, which is not the case for the autoencoder.

![Architecture of the adversarial autoencoder. The loss function of the autoencoder is used as an input to the adversary for it to discriminate the smeared background samples.[]{data-label="fig:advAE"}](figures/AutoEncFigure_horizontal.pdf){width="\textwidth"}

We now combine 