The statistical combination of disjoint signal regions in reinterpretation studies uses more of the data of an analysis and gives more robust results than the single signal region approach. We present the implementation and usage of signal region combination in MadAnalysis 5 through two methods: an interface to the Pyhf package making use of statistical models in JSON-serialised format provided by the ATLAS collaboration, and a simplified likelihood calculation making use of covariance matrices provided by the CMS collaboration. The gain in physics reach is demonstrated 1.) by comparison with official mass limits for 4 ATLAS and 5 CMS analyses from the Public Analysis Database of MadAnalysis 5 for which signal region combination is currently available, and 2.) by a case study for an MSSM scenario in which both stops and sbottoms can be produced and have a variety of decays into charginos and neutralinos.
Cited by 3
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Goodsell et al., Active learning BSM parameter spaces
Eur. Phys. J. C 83, 268 (2023) [Crossref]
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Université Grenoble Alpes / Grenoble Alpes University [UGA]
- 2 Laboratoire d'Annecy-le-Vieux de Physique Théorique [LAPTh]
- 3 Durham University
- 4 Laboratoire de Physique Théorique et Hautes Energies / Laboratory of Theoretical and High Energy Physics [LPTHE]