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A data-based parametrization of parton distribution functions
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 ove...
Autores principales: | , , |
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Lenguaje: | eng |
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2021
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1140/epjc/s10052-022-10136-z http://cds.cern.ch/record/2789763 |
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author | Carrazza, Stefano Cruz-Martinez, Juan M. Stegeman, Roy |
author_facet | Carrazza, Stefano Cruz-Martinez, Juan M. Stegeman, Roy |
author_sort | Carrazza, Stefano |
collection | CERN |
description | 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. |
id | cern-2789763 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2021 |
record_format | invenio |
spelling | cern-27897632023-01-31T09:04:44Zdoi:10.1140/epjc/s10052-022-10136-zhttp://cds.cern.ch/record/2789763engCarrazza, StefanoCruz-Martinez, Juan M.Stegeman, RoyA data-based parametrization of parton distribution functionshep-phParticle Physics - PhenomenologySince 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.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.arXiv:2111.02954TIF-UNIMI-2021-18oai:cds.cern.ch:27897632021-11-04 |
spellingShingle | hep-ph Particle Physics - Phenomenology Carrazza, Stefano Cruz-Martinez, Juan M. Stegeman, Roy A data-based parametrization of parton distribution functions |
title | A data-based parametrization of parton distribution functions |
title_full | A data-based parametrization of parton distribution functions |
title_fullStr | A data-based parametrization of parton distribution functions |
title_full_unstemmed | A data-based parametrization of parton distribution functions |
title_short | A data-based parametrization of parton distribution functions |
title_sort | data-based parametrization of parton distribution functions |
topic | hep-ph Particle Physics - Phenomenology |
url | https://dx.doi.org/10.1140/epjc/s10052-022-10136-z http://cds.cern.ch/record/2789763 |
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