  We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the
problem of detecting new physics processes in proton-proton collisions at the
Large Hadron Collider. Anomaly detection based on ALAD matches performances
reached by Variational Autoencoders, with a substantial improvement in some
cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show
how a data-driven anomaly detection and characterization would work in real
life, re-discovering the top quark by identifying the main features of the
t-tbar experimental signature at the LHC.
