central line is the overall AUC score. All model hyperparameters are left unchanged during the training, with only the relative fraction of corruption changing, defined as a percentage of the total training set. From the plot it is clear that injecting signal events during training has little effect on the overall performance until the fraction of corruption becomes unrealistically large, showing the potential applicability of the method to real data.

![Effect of contaminating the training sample with an increasing fraction of signal events. The central line shows the overall AUC score, and the band represents the difference between the upper and lower AUC scores.[]{data-label="fig:corruption"}](figures/corr.pdf)

Since the performance is not drastically affected by a corruption of the training data, we can proceed with a training sample consisting purely of background events. One of the advantages of the autoencoder only needing to be trained on background events is that it can then be tested for signal events arising from any model. Here, we test our adversarially-trained autoencoder on a variety of different new physics models. We aim to quantify the effect of the resonance’s quantum numbers, i.e. spin, colour and coupling strengths, on the performance of the autoencoder. The models used are:

