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.

A data-based parametrization of parton distribution functions / S. Carrazza, C. Juan, R. Stegeman. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - 82:(2022 Feb 22), pp. 163.1-163.11. [10.1140/epjc/s10052-022-10136-z]

A data-based parametrization of parton distribution functions

S. Carrazza
Primo
;
C. Juan
Penultimo
;
R. Stegeman
Ultimo
2022

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 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.
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
H2020_ERC17SFORT_01 - Proton strucure for discovery at the Large Hadron Collider (NNNPDF) - FORTE, STEFANO - H2020_ERC - Horizon 2020_Europern Research Council - 2017
https://doi.org/10.1140/epjc/s10052-022-10136-z
Article (author)
File in questo prodotto:
File Dimensione Formato  
s10052-022-10136-z.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 1.29 MB
Formato Adobe PDF
1.29 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/910757
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact