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Emergence of pion parton distributions

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arxiv 2201.00884 v2 pith:3SIBZHSD submitted 2022-01-03 hep-ph hep-exhep-latnucl-exnucl-th

classification hep-phhep-exhep-latnucl-exnucl-th
keywords pionboundsconsistentderivedpartonpredictionsvalence-quarkall-orders
verification ladder T0 review T1 audit T2 compute T3 formal
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Supposing only that there is an effective charge which defines an evolution scheme for parton distribution functions (DFs) that is all-orders exact, strict lower and upper bounds on all Mellin moments of the valence-quark DFs of pion-like systems are derived. Exploiting contemporary results from numerical simulations of lattice-regularised quantum chromodynamics (QCD) that are consistent with these bounds, parameter-free predictions for pion valence, glue, and sea DFs are obtained. The form of the valence-quark DF at large values of the light-front momentum fraction is consistent with predictions derived using the QCD-prescribed behaviour of the pion wave function.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Gradient flow for parton distribution functions: first application to the pion

    hep-lat 2025-09 conditional novelty 6.0 of 10

    Pion PDF moment ratios up to <x^5> were extracted from lattice QCD with gradient flow and agree with phenomenological fits.

  2. Pion parton distribution functions and pion-nucleus induced $J/\psi$ production in extended light-front holographic QCD

    hep-ph 2026-05 unverdicted novelty 5.0 of 10

    Pion PDFs from holographic light-front QCD are consistent with global analyses and produce J/ψ cross sections matching experimental data across energies and targets.

  3. Pion parton distribution functions and pion-nucleus induced $J/\psi$ production in extended light-front holographic QCD

    hep-ph 2026-05 unverdicted novelty 4.0 of 10

    Pion PDFs derived from holographic light-front wave functions yield NLO J/ψ production cross sections in pion-nucleus collisions that agree with data.

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