Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
This work addresses anomaly detection for new physics discovery in high-energy physics experiments, but it is incremental as it applies an existing method to a specific dataset.
The researchers tackled the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider by applying an Adversarially Learned Anomaly Detection (ALAD) algorithm, achieving performance comparable to Variational Autoencoders with substantial improvements in some cases and successfully re-discovering the top quark by identifying key features of its experimental signature.
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.