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

TL;DR

This paper employs neural network-based QCD analysis to extract charged hadron fragmentation functions from combined electron-positron and SIDIS data, achieving improved flavor separation and agreement with existing models.

Contribution

It introduces a neural network approach for NLO QCD analysis of fragmentation functions using combined SIA and SIDIS data, including SIDIS flavor dependence constraints.

Findings

Good agreement with JAM20 and NNFF1.1h FF sets

Enhanced flavor separation from SIDIS data

Provides valuable FFs for high-energy processes

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 fitting 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.