A data-based parametrization of parton distribution functions

TL;DR

This paper introduces a new methodology for determining parton distribution functions that removes the traditional common prefactor, simplifying the process while maintaining accuracy and agreement with previous results.

Contribution

It presents a novel approach to eliminate the common prefactor in PDF parametrization, enhancing simplicity without sacrificing performance.

Findings

Good agreement with previous PDF results

Simplified methodology without loss of efficiency

Effective removal of the common prefactor

Abstract

Since the first determination of a structure function many decades ago, all methodologies used to determine structure functions or parton distribution functions (PDFs) have employed a common prefactor as part of the parametrization. The NNPDF collaboration pioneered the use of neural networks to overcome the inherent bias of constraining the space of solution with a fixed functional form while still keeping the same common prefactor as a preprocessing. Over the years various, increasingly sophisticated, techniques have been introduced to counter the effect of the prefactor on the PDF determination. In this paper we present a methodology to remove the prefactor entirely, thereby significantly simplifying the methodology, without a loss of efficiency and finding good agreement with previous results.