Nuclear corrections on the charged hadron fragmentation functions in a Neural Network global QCD analysis

In this work, we present the new global QCD analyses, referred to as PKHFF.23, for charged pion, kaon, and unidentified light hadrons by utilizing the Neural Network for fitting the high energy lepton-lepton and lepton-hadron scattering to determine parton-to-hadron fragmentation functions (FFs) at both next-to-leading-order (NLO) and next-to-next-to-leading-order (NNLO) accuracy. The analyses include all available single-inclusive $e^+e^-$ annihilation (SIA) and semi-inclusive deep-inelastic scattering (SIDIS) data for charged pions, kaons, and unidentified light hadrons. Considering the most recent nuclear parton distribution functions (nuclear PDFs) available in the literature, we assess the impact of nuclear corrections on the determination of light hadrons FFs. We show that considering the nuclear corrections at both NLO and NNLO accuracy affect the central values of FFs and the associated uncertainty bands, and could improve the fit quality as well. The Neural Network parametrization enriched with the Monte Carlo methodology for uncertainty estimations are used for all sources of experimental uncertainties and the proton PDFs.