Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908488891891712 |
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| author | Gambhir, Rikab Mastandrea, Radha Nachman, Benjamin Thaler, Jesse |
| author_facet | Gambhir, Rikab Mastandrea, Radha Nachman, Benjamin Thaler, Jesse |
| contents | We present the first study of anti-isolated Upsilon decays to two muons ($Υ\to μ^+ μ^-$) 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 multi-feature likelihood compared to traditional "cut-and-count" methods. Our work 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data Gambhir, Rikab Mastandrea, Radha Nachman, Benjamin Thaler, Jesse High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability We present the first study of anti-isolated Upsilon decays to two muons ($Υ\to μ^+ μ^-$) 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 multi-feature likelihood compared to traditional "cut-and-count" methods. Our work 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. |
| title | Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2502.14036 |