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

To gain a comprehensive view of what the LHC tells us about physics beyond the Stan-dard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general search-analyses are not statistically orthogonal, so performing compre-hensive 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 combina-tion 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.

Automated summary

In order to understand the impact of LHC on physics beyond the Standard Model, it is important to combine different BSM-sensitive analyses. However, these analyses are not statistically independent, so it is crucial to determine the extent of overlap between their signal regions. We propose a stochastic method and a graph algorithm to efficiently find signal region combinations with no mutual overlap, optimizing upper limits on BSM-model cross-sections. We demonstrate the increased exclusion power compared to single-analysis limits using models of varying complexity, ranging from simplified models to a 19-dimensional supersymmetric model.