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arXiv · 2209.00025

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

Jack Y. Araz·Andy Buckley·Benjamin Fuks·Humberto Reyes-Gonzalez·Wolfgang Waltenberger·Sophie L. Williamson·Jamie Yellen
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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.

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Jack Y. Araz, Andy Buckley, Benjamin Fuks, Humberto Reyes-Gonzalez, Wolfgang Waltenberger, Sophie L. Williamson, Jamie Yellen. 2022-08-31. Strength in numbers: optimal and scalable combination of LHC new-physics searches. https://doi.org/10.21468/scipostphys.14.4.077

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