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DOE OSTI · 2583235

Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

Gambhir, Rikab [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (United States)]·Mastandrea, Radha [Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000252876755)·Nachman, Benjamin [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States); Stanford Univ., CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000310240932)·Thaler, Jesse [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (United States)] (ORCID:0000000224068160)
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Abstract

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.

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Gambhir, Rikab [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (United States)], Mastandrea, Radha [Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000252876755), Nachman, Benjamin [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States); Stanford Univ., CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000310240932), Thaler, Jesse [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (United States)] (ORCID:0000000224068160). 2025-07-08. Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data. https://doi.org/10.1103/vvv3-5kkl

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