• DOI:10.1140/epjc/s10052-022-10136-z
• Corpus ID: 242757517

A data-based parametrization of parton distribution functions

@article{Carrazza2021ADP,
  title={A data-based parametrization of parton distribution functions},
  author={Stefano Carrazza and Juan Cruz-Martinez and Roy Stegeman},
  journal={The European Physical Journal C},
  year={2021},
  volume={82}
}

• S. Carrazza, J. Cruz-Martinez, Roy Stegeman
• Published 4 November 2021
• Computer Science
• The European Physical Journal C

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 perform a data-based scaling of the Bjorken x input parameter which facilitates the removal the prefactor, thereby significantly simplifying the methodology, without a loss of efficiency and finding good agreement with previous results.