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arxiv: 1901.11430 · v2 · submitted 2019-01-31 · ⚛️ nucl-th · hep-ph

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Parton Distribution Functions from a Light Front Hamiltonian and QCD Evolution for Light Mesons

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classification ⚛️ nucl-th hep-ph
keywords distributionfunctionslightpartonpionconsistentdataevolution
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We obtain the pion and the kaon parton distribution functions from the eigenstates of a light front effective Hamiltonian in the constituent quark-antiquark representation suitable for low-momentum scale applications. By taking these scales as the only free parameters, the valence quark distribution functions of the pion, after QCD evolution, are consistent with the data from the FNAL-E615 experiment. The ratio of the up quark distribution of the kaon to that of the pion also agrees with the CERN-NA3 experiment. Supplemented by known parton distribution functions for the nucleons, we further obtain the cross section consistent with experimental data for the ${\pi^-\rm{nucleus}}\rightarrow{\mu^+\mu^-X}$ Drell-Yan process.

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Cited by 3 Pith papers

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

  1. 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

    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.

  2. Light-front mass operator with dressed quarks

    hep-ph 2026-04 unverdicted novelty 5.0

    An effective dressed light-front mass operator for quarks is constructed from a lattice-inspired propagator and applied to compute pion TMDs, PDFs, and distribution amplitudes.

  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

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