Speaker
Description
The search for physics beyond the standard model is one of the prime focuses in high-energy physics. Conventional searches at the LHC, though comprehensive, have not yet shown signs for new physics. Machine learning based anomaly detection has emerged offering a model-agnostic way to enhance the sensitivity of generic searches as compared to those targeting specific signal model. CATHODE (Classifying Anomalies THrough Outer Density Estimation), one of these methods, is a two-step method that combines a data driven background density estimation with a classifier flagging potential signal.
These machine learning methods have shown promising improvements although to date, most studies have mainly focused on dijet resonances. We extend CATHODE to signals with multiple decay modes and varying jet multiplicities, demonstrating its first application to multi-jet resonances and improving the robustness of weakly supervised anomaly detection beyond the dijet regime.