the autoencoder with an adversarial network to improve the reliability and robustness of this unsupervised-learning approach. To achieve the aim of the autoencoder being able to make its predictions independent of the smearing of the background, we use the autoencoder loss as an input to the adversary. Since a threshold on the autoencoder loss is used to perform the classification between signal and background, it is completely analogous to the output of the dedicated classifier used above, on which a cut is placed to classify the events. This input is then followed by two hidden layers each with 20 nodes and ReLu activation functions, with three softmax output nodes and a categorical cross entropy loss. This architecture is illustrated by the diagram in Fig. \[fig:advAE\]. The training proceeds similarly to the adversarial classifier, but with only background events in the training sample:

1.  The autoencoder is trained for three epochs using the Adam optimiser with a learning rate of 0.01 and a batch size of 500.

2.  The adversary is trained for three epochs using mini-batch gradient descent with a learning rate of 0.01 and a batch size of 500.

3.  The autoencoder is trained for one epoch with mini-batch 