Strength in numbers: Optimal and scalable combination of LHC new-physics searches
- Author(s)
- Jack Y. Araz, Andy Buckley, Benjamin Fuks, Humberto Reyes-Gonzalez, Wolfgang Waltenberger, Sophie L. Williamson, Jamie Yellen
- 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.
- Organisation(s)
- Particle Physics
- External organisation(s)
- University of Glasgow, Università degli Studi di Genova, Österreichische Akademie der Wissenschaften (ÖAW), Karlsruher Institut für Technologie, Durham University, Sorbonne Université, Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Genova
- Journal
- SciPost Physics
- Volume
- 14
- No. of pages
- 30
- ISSN
- 2542-4653
- DOI
- https://doi.org/10.48550/arXiv.2209.00025
- Publication date
- 04-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103012 High energy physics
- ASJC Scopus subject areas
- Physics and Astronomy(all)
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/strength-in-numbers-optimal-and-scalable-combination-of-lhc-newphysics-searches(bc6a1380-cfec-46b1-a9ea-291cdc909887).html