can be used as anomaly detectors to search for new physics since they only need to be trained on the background.

To this aim, we consider an autoencoder constructed from three hidden layers with 10, 3 and 10 nodes respectively, each with sigmoid activation functions. After the hidden layers, there is a linear output layer with the same dimension as the number of inputs, which correspond to the 21 observables. The loss is the mean squared error between the inputs and outputs—namely, the autoencoder has the goal of reconstructing the inputs as well as possible, having encoded the information into the latent-compressed layer. We train the autoencoder on the three background samples using the Adam optimiser with a learning rate of 0.01 for 500 epochs, and the results are shown in Fig. \[fig:AE\]. Since the autoencoder is trained only on the background events, it learns how to reconstruct background events better than the signal events, and so the distribution of the losses for the signal events in Fig. \[fig:AEresponse\] is at higher values. The ROC curves in Fig. \[fig:AEROC\] are obtained by performing a cut on the loss function and labelling all events above the cut as signal events, and 