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

Autor(en)

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

Abstrakt

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(en)

Teilchenphysik

Externe Organisation(en)

University of Glasgow, Università degli Studi di Genova, Österreichische Akademie der Wissenschaften (ÖAW), Karlsruher Institut für Technologie, Durham University, Icm & Sorbonne University, Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Genova

Journal

SciPost Physics

Band

14

Anzahl der Seiten

30

ISSN

2542-4653

DOI

https://doi.org/10.48550/arXiv.2209.00025

Publikationsdatum

04-2023

Peer-reviewed

Ja

ÖFOS 2012

103012 Hochenergiephysik

ASJC Scopus Sachgebiete

Physics and Astronomy(all)

Link zum Portal

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