Combining Resonant and Tail-based Anomaly Detection

TL;DR

This paper integrates resonant and tail-based anomaly detection techniques using machine learning to identify supersymmetry signals in high-energy physics, demonstrating competitive performance with broader parameter coverage.

Contribution

It introduces a novel combination of resonant and tail-based anomaly detection methods applied to supersymmetry scenarios, leveraging the CATHODE machine learning approach.

Findings

CATHODE is competitive with dedicated cut-based searches.

The method covers a wider parameter space.

It effectively detects gluino pair production signals.

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 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 $H_T$ distributions, making this a novel combination of resonant and tail-based anomaly detection.