Fitting Parton Distribution Data with Multiplicative Normalization Uncertainties

TL;DR

This paper introduces an unbiased iterative method for fitting multiple data sets with multiplicative normalization uncertainties, improving accuracy in determining parton distribution functions using the NNPDF Monte Carlo approach.

Contribution

The paper develops a self-consistent iterative technique to eliminate biases in global fits involving multiplicative uncertainties, enhancing the reliability of parton distribution function extraction.

Findings

The new method removes systematic biases present in traditional approaches.

Application to NNPDF demonstrates improved fit accuracy.

Method is general and applicable to various data fitting scenarios.

Abstract

We consider the generic problem of performing a global fit to many independent data sets each with a different overall multiplicative normalization uncertainty. We show that the methods in common use to treat multiplicative uncertainties lead to systematic biases. We develop a method which is unbiased, based on a self--consistent iterative procedure. We demonstrate the use of this method by applying it to the determination of parton distribution functions with the NNPDF methodology, which uses a Monte Carlo method for uncertainty estimation.