Statistical Patterns of Theory Uncertainties

TL;DR

This paper analyzes the patterns of theoretical uncertainties in LHC cross-section predictions, revealing stochastic-like behavior for strong interactions and proposing an improved estimation scheme to reduce outliers.

Contribution

It introduces a novel method for estimating theoretical uncertainties based on scale variations, improving accuracy for electroweak processes.

Findings

Uncertainty patterns resemble stochastic processes for strong interactions.

Systematic underestimation of uncertainties for electroweak processes.

Proposed scheme reduces outliers in perturbative order mappings.

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.