the autoencoder to signal samples will result in a modified value for the loss function, as some kinematic features differ between signal and background. The depth of the networks and the width of the bottleneck are hyperparameters of the network that can be optimised for the problem at hand. Using autoencoders for anomaly detection, we will show that adversarially-trained neural networks can take systematic uncertainties into account and desensitise the classification performance in data-driven searches for new physics. To achieve this, we adversarially train an autoencoder on Monte-Carlo-generated pseudo-data which has been systematically smeared in order for it to learn to reconstruct the events without using any information about the smearing.

We apply this framework to resonance searches, i.e. a heavy colour-singlet scalar, colour-octet scalar and colour-singlet vector, that are well-motivated by many new physics models. This selection allows one to study the impact of the spin and colour quantum numbers of the resonances on the classification efficiencies.[^2] The resonances are assumed to subsequently decay into top quarks [@Chatrchyan:2012ku; @Aaboud:2018mjh; @Aaboud:2019roo; @Aaboud:2017hnm]. Top quark samples are an ideal playground for anomaly detection, as they can be purified to a very high degree, i.e. the confidence that one trains on a 