NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order

Abstract We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering (DIS), and of W -boson, single-inclusive, and di-jet...

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Main Authors: Juan Cruz-Martinez, Toon Hasenack, Felix Hekhorn, Giacomo Magni, Emanuele R. Nocera, Tanjona R. Rabemananjara, Juan Rojo, Tanishq Sharma, Gijs van Seeventer
Format: Article
Language:English
Published: SpringerOpen 2025-07-01
Series:Journal of High Energy Physics
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Online Access:https://doi.org/10.1007/JHEP07(2025)168
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author Juan Cruz-Martinez
Toon Hasenack
Felix Hekhorn
Giacomo Magni
Emanuele R. Nocera
Tanjona R. Rabemananjara
Juan Rojo
Tanishq Sharma
Gijs van Seeventer
author_facet Juan Cruz-Martinez
Toon Hasenack
Felix Hekhorn
Giacomo Magni
Emanuele R. Nocera
Tanjona R. Rabemananjara
Juan Rojo
Tanishq Sharma
Gijs van Seeventer
author_sort Juan Cruz-Martinez
collection DOAJ
description Abstract We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering (DIS), and of W -boson, single-inclusive, and di-jet production asymmetries in longitudinally polarised proton-proton collisions. The determination is accurate to next-to-next-to-leading order in the strong coupling, and includes heavy quark mass corrections in the analysis of DIS data. Uncertainties due to missing higher-order corrections are systematically incorporated by means of a covariance matrix determined by scale variations. NNPDFpol2.0 is based on a machine learning methodology, that makes use of Monte Carlo sampling for the representation of uncertainties into PDFs, of a neural network for the parametrisation of PDFs, of stochastic gradient descent for the optimisation of PDF parameters, and of hyperoptimisation for the selection of the best fitting model. We study the impact on PDFs of higher-order corrections, of the positivity constraint, and of the data. We demonstrate two phenomenological applications of NNPDFpol2.0, specifically the determination of the proton spin fraction carried by gluons and quarks, and of theoretical predictions for single-hadron production in longitudinally polarised DIS and proton-proton collisions.
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spelling doaj-art-7caf009cbdca45e9a3234d80a9f5e13e2025-08-20T04:01:43ZengSpringerOpenJournal of High Energy Physics1029-84792025-07-012025715310.1007/JHEP07(2025)168NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading orderJuan Cruz-Martinez0Toon Hasenack1Felix Hekhorn2Giacomo Magni3Emanuele R. Nocera4Tanjona R. Rabemananjara5Juan Rojo6Tanishq Sharma7Gijs van Seeventer8CERN, Theoretical Physics DepartmentInstitute for Theoretical Physics, Utrecht UniversityUniversity of Jyvaskyla, Department of PhysicsNikhef Theory GroupDipartimento di Fisica, Università degli Studi di Torino and INFN, Sezione di TorinoNikhef Theory GroupNikhef Theory GroupDipartimento di Fisica, Università degli Studi di Torino and INFN, Sezione di TorinoInstitute for Theoretical Physics, Utrecht UniversityAbstract We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering (DIS), and of W -boson, single-inclusive, and di-jet production asymmetries in longitudinally polarised proton-proton collisions. The determination is accurate to next-to-next-to-leading order in the strong coupling, and includes heavy quark mass corrections in the analysis of DIS data. Uncertainties due to missing higher-order corrections are systematically incorporated by means of a covariance matrix determined by scale variations. NNPDFpol2.0 is based on a machine learning methodology, that makes use of Monte Carlo sampling for the representation of uncertainties into PDFs, of a neural network for the parametrisation of PDFs, of stochastic gradient descent for the optimisation of PDF parameters, and of hyperoptimisation for the selection of the best fitting model. We study the impact on PDFs of higher-order corrections, of the positivity constraint, and of the data. We demonstrate two phenomenological applications of NNPDFpol2.0, specifically the determination of the proton spin fraction carried by gluons and quarks, and of theoretical predictions for single-hadron production in longitudinally polarised DIS and proton-proton collisions.https://doi.org/10.1007/JHEP07(2025)168Parton DistributionsSpecific QCD Phenomenology
spellingShingle Juan Cruz-Martinez
Toon Hasenack
Felix Hekhorn
Giacomo Magni
Emanuele R. Nocera
Tanjona R. Rabemananjara
Juan Rojo
Tanishq Sharma
Gijs van Seeventer
NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
Journal of High Energy Physics
Parton Distributions
Specific QCD Phenomenology
title NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
title_full NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
title_fullStr NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
title_full_unstemmed NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
title_short NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
title_sort nnpdfpol2 0 a global determination of polarised pdfs and their uncertainties at next to next to leading order
topic Parton Distributions
Specific QCD Phenomenology
url https://doi.org/10.1007/JHEP07(2025)168
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