pure $t\bar{t}$ sample is very high, in particular when one top decays hadronically while the other decays leptonically. On the other hand, top final states are complex, consisting of many jets, leptons and missing transverse energy. Thus, uncertainties on reconstructed observables due to detector effects can be large.

The paper is structured as follows: in Sec. \[sec:setup\] we first discuss the analysis setup. To establish a baseline of what can be achieved by supervised learning, we show the performance of a neural network classifier and the effect of combining it with an adversarial neural network in Sec. \[sec:supervised\]. In Sec. \[sec:unsupervised\] we extend this approach to an unsupervised autoencoder for anomaly detection and consider its application to other new physics models and the effects of an impure training sample. We offer conclusions on our findings in Sec. \[sec:conclusions\].

Analysis setup and smearing procedure {#sec:setup}
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We use <span style="font-variant:small-caps;">MadGraph5\_aMC@NLO</span> [@Alwall:2014hca] to generate the events for the study, followed by <span style="font-variant:small-caps;">Pythia 8.2</span> for parton shower and hadronisation. The background events consist of $pp \rightarrow t\bar{t}$ at a centre-of-mass energy of $14$ TeV, with one top quark forced to decay leptonically and the other hadronically. The signal events are generated from 