Beyond Scale Variations: Perturbative Theory Uncertainties from Nuisance Parameters
Frank J. Tackmann

TL;DR
This paper introduces a novel method for estimating perturbative theory uncertainties using nuisance parameters, enabling more accurate, correlated, and data-constrained uncertainty assessments in high-energy physics calculations.
Contribution
The authors develop a parametric approach to quantify theory uncertainties with nuisance parameters, improving upon scale variation methods and allowing data-driven reduction of uncertainties.
Findings
The method captures correlations across spectra and processes.
It enables the use of partial higher-order information.
Application to Drell-Yan $q_T$ spectrum demonstrates improved uncertainty estimation.
Abstract
We develop a new approach to estimate the uncertainty due to missing higher orders in perturbative predictions (the perturbative "theory uncertainty"), which overcomes many inherent limitations of the currently prevalent methods based on varying unphysical renormalization scales. In our approach, the true underlying sources of the theory uncertainty, namely the missing higher-order terms, are identified and parameterized in terms of mutually independent theory nuisance parameters (TNPs). The TNPs are true parameters of the calculation, i.e., they have a well-defined true value that is not or only imprecisely known. This approach affords the theory uncertainty all benefits of a truly parametric uncertainty: It provides correct correlations and allows for consistent error propagation and combination. Furthermore, the TNPs can be profiled in fits, allowing the data to reduce the theory…
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