City Research Online

Abstract

To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general search-analyses are not statistically orthogonal, so performing comprehensive combinations requires knowledge of the extent to which the same events co-populate multiple analyses’ signal regions. We present a novel, stochastic method to determine this degree of overlap, and a graph algorithm to efficiently find the combination of signal regions with no mutual overlap that optimises expected upper limits on BSM-model cross-sections. The gain in exclusion power relative to single-analysis limits is demonstrated with models with varying degrees of complexity, ranging from simplified models to a 19-dimensional supersymmetric model.

Publication Type:	Article
Additional Information:	Copyright J. Y. Araz et al. This work is licensed under the Creative Commons Attribution 4.0 International License. Published by the SciPost Foundation.
Subjects:	Q Science > QC Physics
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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