“School of Particles”
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| Paper IPM / Particles / 17381 |
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In this work, we present a new global QCD analyses, referred to as PKHFF.23, for charged pion, kaon, and unidentified
light hadrons. We utilize a Neural Network to fit the high-energy lepton-lepton and lepton-hadron scattering data, enabling us
to determine parton-to-hadron fragmentation functions (FFs) at next-to-leading-order (NLO) accuracy. The analyses include all
available single-inclusive e+eâ annihilation (SIA) and semi-inclusive deep-inelastic scattering (SIDIS) data from the COMPASS
Collaboration for charged pions, kaons, and unidentified light hadrons. Taking into account themost recent nuclear parton distribution
functions (nuclear PDFs) available in the literature,we evaluate the effect of nuclear corrections on the determination of light hadrons
FFs. The Neural Network parametrization, enriched with the Monte Carlo methodology for uncertainty estimations, is employed
for all sources of experimental uncertainties and the proton PDFs. Our results indicate that incorporating nuclear corrections has
a marginal impact on the central values of FFs and their corresponding uncertainty bands. The inclusion of such corrections does
not significantly affect the fit quality of the data as well. The study suggests that while nuclear corrections are a consideration, their
impact in such QCD analysis is limited.
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