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://ucrisportal.univie.ac.at/en/publications/strength-in-numbers-optimal-and-scalable-combination-of-lhc-newphysics-searches(bc6a1380-cfec-46b1-a9ea-291cdc909887).html