Statistical Patterns of Theory Uncertainties
Abstract
A comprehensive uncertainty estimation is vital for the precision program of the LHC. While experimental uncertainties are often described by stochastic processes and well-defined nuisance parameters, theoretical uncertainties lack such a description. We study uncertainty estimates for cross-section predictions based on scale variations across a large set of processes. We find patterns similar to a stochastic origin, with accurate uncertainties for processes mediated by the strong force, but a systematic underestimate for electroweak processes. We propose an improved scheme, based on the scale variation of reference processes, which reduces outliers in the mapping from leading order to next-to-leading-order in perturbation theory.
Cite
@article{arxiv.2210.15167,
title = {Statistical Patterns of Theory Uncertainties},
author = {Aishik Ghosh and Benjamin Nachman and Tilman Plehn and Lily Shire and Tim M. P. Tait and Daniel Whiteson},
journal= {arXiv preprint arXiv:2210.15167},
year = {2023}
}
Comments
UCI-HEP-TH-2022-21