Combining resonant and tail-based anomaly detection

Journal Article · 22 May 2024 · Physical Review. D.

DOI: https://doi.org/10.1103/PhysRevD.109.096031 · OSTI ID:2361142

Bickendorf, Gerrit ; Drees, Manuel; Kasieczka, Gregor; Krause, Claudius ; Shih, David

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 () 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 $H_T$ distributions, making this a novel combination of resonant and tail-based anomaly detection. Published by the American Physical Society 2024

• Sponsoring Organization: USDOE
• OSTI ID: 2361142
• Journal Information: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 9 Vol. 109; ISSN PRVDAQ; ISSN 2470-0010
• Publisher: American Physical SocietyCopyright Statement
• Country of Publication: United States
• Language: English