Pion and kaon fragmentation functions at next-to-next-to-leading order

TL;DR

This paper provides a precise determination of pion and kaon fragmentation functions using NNLO QCD corrections, neural networks, and Monte Carlo methods, improving the understanding of hadronization processes in particle physics.

Contribution

It introduces a novel NNLO analysis of fragmentation functions combining neural networks and Monte Carlo uncertainty estimation, enhancing accuracy over previous NLO studies.

Findings

Fragmentation functions are determined with improved precision.

Higher order corrections significantly affect the results.

Uncertainty estimates are robust due to the Monte Carlo approach.

Abstract

We present a new determination of unpolarised charged pion and kaon fragmentation functions from a set of single-inclusive electron-positron annihilation and lepton-nucleon semi-inclusive deep-inelastic scattering data. The determination includes next-to-next-to-leading order QCD corrections to both processes, and is carried out in a framework that combines a neural-network parametrisation of fragmentation functions with a Monte Carlo representation of their uncertainties. We discuss the quality of the determination, in particular its dependence on higher order corrections.