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

Araz, Jack Y.; Buckley, Andy; Fuks, Benjamin; Reyes-González, Humberto; Waltenberger, Wolfgang; Williamson, Sophie L.; Yellen, Jamie

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.

Citation

Araz, J. Y., Buckley, A., Fuks, B., Reyes-González, H., Waltenberger, W., Williamson, S. L., & Yellen, J. (2023). Strength in numbers: Optimal and scalable combination of LHC new-physics searches. SciPost Physics, 14(4), Article 077. https://doi.org/10.21468/scipostphys.14.4.077

• Journal Article Type: Article
• Acceptance Date: Jan 26, 2023
• Online Publication Date: Apr 20, 2023
• Publication Date: Apr 20, 2023
• Deposit Date: Feb 19, 2024
• Publicly Available Date: Feb 19, 2024
• Journal: SciPost Physics
• Print ISSN: 2542-4653
• Publisher: SciPost
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 4
• Article Number: 077
• DOI: https://doi.org/10.21468/scipostphys.14.4.077
• Keywords: General Physics and Astronomy
• Public URL: https://durham-repository.worktribe.com/output/2269710