of di-Higgs final states, or completely unknown processes, e.g. the production of a gluino, are of utmost interest to search for at the LHC. However, obtaining a data-driven training sample for such processes is impossible, thus, limiting the applicability for data-driven methods. One way around this bottleneck is not to train on signal at all, but to identify the kinematic features of background samples and to design a method that flags up events that do not possess the same features, thereby classifying such an event as signal. The remaining residual experimental problem that remains for such an approach are the experimental and systematic uncertainties in the measurements of the inputs of a data-driven anomaly detection method.

Autoencoders [@bangalore; @Kingma:2013hel] have been proposed for denoising [@Vincent:2008:ECR:1390156.1390294], generative models [@Otten:2019hhl] and in particular for anomaly detection [@Farina:2018fyg; @Hajer:2018kqm; @Heimel:2018mkt; @Roy:2019jae]. They use an information bottleneck to map an input to a latent-compressed representation and then decode this representation back. The loss function measures the squared difference between input and decoded output. By minimising the loss function, the autoencoder learns intrinsic features of the training samples that survive the information bottleneck. After training the autoencoder on background samples, it is expected that applying 