Study of nuclear corrections on the charged hadron fragmentation functions in a Neural Network global QCD analysis

Abstract: 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 $e^+e^-$ 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 the most 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.