4.7 Article

Simultaneous Monte Carlo analysis of parton densities and fragmentation functions

Journal

PHYSICAL REVIEW D
Volume 104, Issue 1, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.104.016015

Keywords

-

Funding

  1. US Department of Energy [DE-AC05-06OR23177]
  2. DOE, Office of Science, Office of Nuclear Physics in the Early Career Program
  3. U.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE-SC0018106]
  4. U.S. Department of Energy (DOE) [DE-SC0018106] Funding Source: U.S. Department of Energy (DOE)

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The study performs a comprehensive Monte Carlo analysis of high-energy scattering data to determine parton distribution functions and parton to hadron fragmentation functions in the proton. By employing a new fitting strategy and parametrizations, the analysis evaluates the impact of different datasets on sea quark densities and confirms the suppression of the strange quark distribution previously observed. This new fit, referred to as JAM20-SIDIS, will allow for improved studies of parton correlation functions and the matching of collinear to TMD factorization descriptions across various processes.
We perform a comprehensive new Monte Carlo analysis of high-energy lepton-lepton, lepton-hadron and hadron-hadron scattering data to simultaneously determine parton distribution functions (PDFs) in the proton and parton to hadron fragmentation functions (FFs). The analysis includes all available semi-inclusive deep-inelastic scattering and single-inclusive e(+)e(-) annihilation data for pions, kaons and unidentified charged hadrons, which allows the flavor dependence of the fragmentation functions to be constrained. Employing a new multistep fitting strategy and more flexible parametrizations for both PDFs and FFs, we assess the impact of different datasets on sea quark densities and confirm the previously observed suppression of the strange quark distribution. The new fit, which we refer to as JAM20-SIDIS, will allow for improved studies of universality of parton correlation functions, including transverse momentum dependent (TMD) distributions, across a wide variety of process, and the matching of collinear to TMD factorization descriptions.

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