Abstract
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 excess 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.
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Acknowledgments
The authors would like to acknowledge funding through the SNSF Sinergia grant CR-SII5_193716 “Robust Deep Density Models for High-Energy Particle Physics and Solar Flare Analysis (RODEM)” and the SNSF project grant 200020_212127 “At the two upgrade frontiers: machine learning and the ITk Pixel detector”. The research of MK is supported by the DFG under grant 396021762 – TRR 257: Particle Physics Phenomenology after the Higgs Discovery.
Code availability. The code used to produce all results presented in this paper is available at https://github.com/IvanOleksiyuk/jet_cluster_scanning.
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Oleksiyuk, I., Raine, J.A., Krämer, M. et al. Cluster Scanning: a novel approach to resonance searches. J. High Energ. Phys. 2024, 163 (2024). https://doi.org/10.1007/JHEP06(2024)163
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DOI: https://doi.org/10.1007/JHEP06(2024)163