of anomalous events can be realised using an autoencoder, trained on a pure background sample. However, even in this ideal scenario, residual uncertainties due to the imperfect reconstruction of final-state objects remain.

Focusing on resonance searches in semileptonic $t\bar{t}$ final states, we quantified the performance of an adversarially-trained autoencoder. In particular, we compared the performance of an autoencoder-based unsupervised-learning approach with a supervised neural network classifier. While the supervised classifier performs significantly better than the unsupervised-learning approach, the latter still shows a strong aptitude in telling apart signal from background events. In both cases reconstruction uncertainties show however a big impact on the evaluated performance of the classifiers, thereby evidencing the need for measures to desensitise them against such uncertainties for a reliable performance evaluation.

We proposed to combine the autoencoder with an adversarial neural network to realise a robust and reliable unsupervised anomaly detection method that can be readily applied to experimental data. The classification result is independent of the smearing of the reconstructed final-state objects over the entire range of the ROC curve and even extends to training on corrupted backgrounds, i.e. backgrounds with a large admixture of signal events. Although we applied it to Monte-Carlo-generated pseudo-data, we 