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

TL;DR

This paper introduces a stochastic and graph-based method to optimally combine LHC BSM searches, improving exclusion limits by accounting for overlaps between analyses.

Contribution

It presents a novel stochastic approach and a graph algorithm for combining non-orthogonal LHC analyses to enhance sensitivity to new physics.

Findings

Improved exclusion limits on BSM models.

Effective combination of multiple analyses with overlaps.

Demonstrated method on complex supersymmetric models.

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.