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We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to theproblem of detecting new physics processes in proton-proton collisions at theLarge Hadron Collider. Anomaly detection based on ALAD matches performancesreached by Variational Autoencoders, with a substantial improvement in somecases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we showhow a data-driven anomaly detection and characterization would work in reallife, re-discovering the top quark by identifying the main features of thet-tbar experimental signature at the LHC.

Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark