The production of and mesons in lead-lead (Pb-Pb) and proton-proton ( ) collisions is studied in their dimuon decay channel using the CMS detector at the LHC. The meson is observed for the first time in Pb-Pb collisions, with a significance above 5 standard deviations. The ratios of yields measured in Pb-Pb and collisions are reported for both the and mesons, as functions of transverse momentum and Pb-Pb collision centrality. These ratios, when appropriately scaled, are significantly less than unity, indicating a suppression of yields in Pb-Pb collisions. This suppression increases from peripheral to central Pb-Pb collisions. Furthermore, the suppression is stronger for mesons compared to mesons, extending the pattern of sequential suppression of quarkonium states in nuclear collisions previously seen for the , , , and mesons. © 2024 CERN, for the CMS Collaboration2024CERN
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This content will become publicly available on July 1, 2026
Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
We present the first study of anti-isolated Upsilon decays to two muons ( ) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy, we “rediscover” the in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. We elevate the signal significance to using these methods, starting from 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
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- Award ID(s):
- 2019786
- PAR ID:
- 10620787
- 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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