A data-based parametrization of parton distribution functions

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.

10 pages, 7 figures, final version published in EPJC

References in corpus (5)

• LHAPDF6: parton density access in the LHC precision era
• A determination of the fragmentation functions of pions, kaons, and protons with faithful uncertainties
• Precision determination of electroweak parameters and the strange content of the proton from neutrino deep-inelastic scattering
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Cited by in corpus (5)

• Machine Learning and LHC Event Generation
• Snowmass 2021 whitepaper: Proton structure at the precision frontier
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• Nonlinear Corrections to the Momentum Sum Rule
• Evolution of entropy at small $x$