Speaker
Description
The search for physics beyond the standard model is one of the main 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 estimation with a classifier flagging potential signal. To date, most studies have mainly focused on dijet resonances.
Extending this approach, we explore signals with multiple decays modes spanning a range of jet multiplicities, leading to a more challenging detection scenario. We demonstrate how a well established idea from jet-substructure physics (recursive soft drop) can be utilized to perform anomaly detection. We present the first application of CATHODE to multi-jet resonances, which enhances the sensitivity beyond the dijet regime and increases the robustness of weakly supervised anomaly detection, thereby broadening its applicability.