Neural network QCD analysis of charged hadron fragmentation functions in the presence of SIDIS data

• Hadi Hashamipour (School of particles and accelerators, IPM)

We present a QCD analysis to extract the fragmentation functions (FFs) of unidentified light charged hadron entitled as SHK22.h from high-energy lepton-lepton annihilation and lepton-hadron scattering datasets. This analysis includes the data from all available single inclusive electron-positron annihilation processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. We exploit the analytic derivative of the neural network for fitting of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD. The Monte Carlo method is implied for all sources of experimental uncertainties and the parton distribution functions as well.