Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark

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 tt experimental signature at the LHC.

Automated summary

The study applies the ALAD algorithm to detect new physics processes in proton-proton collisions at the Large Hadron Collider, achieving performances comparable to Variational Autoencoders and substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, the study demonstrates a data-driven anomaly detection and characterization in real life, rediscovering the top quark at the LHC.