Combining resonant and tail-based anomaly detection

Abstract

In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g., Z/W/h). This can make these models rich and promising targets for recently developed resonant anomaly detection methods powered by modern machine learning. We demonstrate this using the state-of-the-art classifying anomalies through outer density estimation (cathode) method applied to supersymmetry scenarios with gluino pair production. We show that cathode, despite being model agnostic, is nevertheless competitive with dedicated cut-based searches, while simultaneously covering a much wider region of parameter space. The gluino events also populate the tails of the missing energy and HT distributions, making this a novel combination of resonant and tail-based anomaly detection.