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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 invariant mass spectra 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 3 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.
Oleksiyuk et al. (Tue,) studied this question.
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