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., <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mi>Z</a:mi><a:mo>/</a:mo><a:mi>W</a:mi><a:mo>/</a:mo><a:mi>h</a:mi></a:math>). 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 () method applied to supersymmetry scenarios with gluino pair production. We show that , 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 <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:msub><c:mi>H</c:mi><c:mi>T</c:mi></c:msub></c:math> distributions, making this a novel combination of resonant and tail-based anomaly detection. Published by the American Physical Society 2024

Topics & Concepts