Strength in numbers: Optimal and scalable combination of LHC new-physics searches

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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.
Funding Information
  • Institut National de Physique Nucléaire et de Physique des Particules (Théorie-BSMGA)
  • Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN 20172LNEEZ)
  • Royal Society (RGF\EA\180252)
  • Science and Technology Facilities Council (ST/S000887/1)

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