PDF dependence on parameter fits from hadronic data

TL;DR

This paper discusses methods for extracting parameters like the strong coupling constant from hadronic data, emphasizing the importance of accounting for PDF dependencies to reduce measurement dispersion and proposing a concept of dataset-specific preferred parameter values.

Contribution

It introduces a method to incorporate PDF dependencies in parameter extraction, reducing result dispersion and suggesting dataset-specific preferred parameter values.

Findings

Accounting for PDF dependence reduces measurement dispersion.

Inclusion of PDF effects improves parameter extraction consistency.

Proposes concept of dataset-specific preferred parameter values.

Abstract

We present a discussion on the methods for extracting a given parameter from measurements of hadronic data, with particular focus on determinations of the strong coupling constant. We show that when the PDF dependency on the determination is adequately taken into account, the dispersion between the results from different measurements is significantly reduced. We speculatively propose the concept of preferred value of a parameter from a particular dataset.