Speaker
Description
We present the first study of anti-isolated Upsilon decays to two muons (Υ → μ+μ−) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy (CATHODE), we “rediscover” the Υ in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. CATHODE can elevate the signal significance to 6.4σ (starting from 1.6σ using the dimuon mass spectrum alone), far outperforming classical cut-based methods on individual observables. Our work demonstrates that it is possible and practical to find real signals in experimental collider data using ML-based anomaly detection. Additionally, we distill a readily-accessible benchmark dataset from the CMS Open Data to facilitate future anomaly detection development. THIS TALK IS THE FIRST OF TWO PARTS.
