Cluster Scanning: a novel approach to resonance searches
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913357396705280 |
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| author | Oleksiyuk, Ivan Raine, John Andrew Krämer, Michael Voloshynovskiy, Svyatoslav Golling, Tobias |
| author_facet | Oleksiyuk, Ivan Raine, John Andrew Krämer, Michael Voloshynovskiy, Svyatoslav Golling, Tobias |
| contents | We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to construct a signal-enriched region. The spectra of a selected observable (e.g. invariant mass) in these two regions are then used to determine whether a resonant signal is present. A pseudo-analysis on the LHC Olympics dataset with a $Z'$ resonance shows that Cluster Scanning outperforms the widely used 4-parameter functional background fitting procedures, reducing the number of signal events needed to reach a $3σ$ significant access by a factor of 0.61. Emphasis is placed on the speed of the method, which allows the test statistic to be calibrated on synthetic data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cluster Scanning: a novel approach to resonance searches Oleksiyuk, Ivan Raine, John Andrew Krämer, Michael Voloshynovskiy, Svyatoslav Golling, Tobias High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to construct a signal-enriched region. The spectra of a selected observable (e.g. invariant mass) in these two regions are then used to determine whether a resonant signal is present. A pseudo-analysis on the LHC Olympics dataset with a $Z'$ resonance shows that Cluster Scanning outperforms the widely used 4-parameter functional background fitting procedures, reducing the number of signal events needed to reach a $3σ$ significant access by a factor of 0.61. Emphasis is placed on the speed of the method, which allows the test statistic to be calibrated on synthetic data. |
| title | Cluster Scanning: a novel approach to resonance searches |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.17714 |