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Title: Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
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, we “rediscover” the ϒ in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. We elevate the signal significance to 6.4 σ using these methods, starting from 1.6 σ using the dimuon mass spectrum alone. Moreover, we demonstrate improved sensitivity from using an ML-based estimate of the multifeature likelihood compared to traditional “cut-and-count” methods. This is the first ever detection of anti-isolated Upsilons, which can be useful in the study of heavy-flavor fragmentation in quantum chromodynamics. Our Letter demonstrates that it is possible and practical to find real signals in experimental collider data using ML-based anomaly detection, and we distill a readily accessible benchmark dataset from the CMS Open Data to facilitate future anomaly detection developments. Published by the American Physical Society2025  more » « less
Award ID(s):
2019786
PAR ID:
10620787
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
American Physical Society
Date Published:
Journal Name:
Physical Review Letters
Volume:
135
Issue:
2
ISSN:
0031-9007
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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