Cluster Scanning: a novel approach to resonance searches
arxiv(2024)
摘要
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 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.
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