TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.14.4.077
TI  - Strength in numbers: Optimal and scalable combination of LHC new-physics searches
PY  - 2023/04/20
UR  - https://www.scipost.org/SciPostPhys.14.4.077
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 14
IS  - 4
SP  - 077
A1  - Araz, Jack Y.
AU  - Buckley, Andy
AU  - Fuks, Benjamin
AU  - Reyes-González, Humberto
AU  - Waltenberger, Wolfgang
AU  - Williamson, Sophie L.
AU  - Yellen, Jamie
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.14.4.077,
	title={{Strength in numbers: Optimal and scalable combination of LHC new-physics searches}},
	author={Jack Y. Araz and Andy Buckley and Benjamin Fuks and Humberto Reyes-Gonzalez and Wolfgang Waltenberger and Sophie L. Williamson and Jamie Yellen},
	journal={SciPost Phys.},
	volume={14},
	pages={077},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.14.4.077},
	url={https://scipost.org/10.21468/SciPostPhys.14.4.077},
}