Affiliation:
1. Durham University
2. University of Glasgow
3. Laboratory of Theoretical and High Energy Physics
4. National Institute of Nuclear Physics Genoa Section
5. University of Genoa
6. Austrian Academy of Sciences
7. University of Vienna
8. Karlsruhe Institute of Technology
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.
Funder
Institut National de Physique Nucléaire et de Physique des Particules
Ministero dell’Istruzione, dell’Università e della Ricerca
Royal Society
Science and Technology Facilities Council
Subject
General Physics and Astronomy
Cited by
2 articles.
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