Regular Article - Theoretical Physics
A data-based parametrization of parton distribution functions
TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Milan, Italy
2 Theoretical Physics Department, CERN, 1211, Geneva 23, Switzerland
3 Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab Emirates
Accepted: 11 February 2022
Published online: 22 February 2022
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.
© The Author(s) 2022
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