Neural Network QCD analysis of charged hadron Fragmentation Functions in the presence of SIDIS data

Abstract

In this paper, 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 data sets. This analysis includes the data from all available single inclusive electron-positron annihilation (SIA) processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. The SIDIS data which has been measured by the COMPASS experiment could allow the flavor dependence of the FFs to be well constrained. We exploit the analytic derivative of the Neural Network (NN) for the parametrization of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD (pQCD). The Monte Carlo method is implied for all sources of experimental uncertainties and the Parton distribution functions (PDFs) as well. Very good agreements are achieved between the SHK22.h FFs set and the most recent QCD fits available in literature, namely JAM20 and NNFF1.1h. In addition, we discuss the impact arising from the inclusion of SIDIS data on the extracted light-charged hadron FFs. The global QCD resulting at NLO for charged hadron FFs provides valuable insights for applications in present and future high-energy measurement of charged hadron final state processes.

Machine Learning and LHC Event Generation

“…, where i is a partonic index and (h) a hadronic label. To remove theory bias and model-dependence in the determination of FFs, machine learning techniques can be adopted [58][59][60][61]. Feed-forward neural networks are deployed as universal unbiased interpolants for z D (h) i (z, Q 0 ), whose weight and threshold parameters are obtained from a log-likelihood maximization by comparison with experimental data.…”