First next-to-next-to-leading-order extraction of fragmentation functions for Lambda hyperons

TL;DR

This paper presents the first NNLO global analysis of Lambda hyperon fragmentation functions using diverse experimental data and neural network parametrization, providing new insights into strange baryon hadronisation.

Contribution

It introduces a novel NNLO analysis with neural network parametrization for Lambda fragmentation functions, including for the first time a separate valence-quark determination.

Findings

First NNLO extraction of Lambda fragmentation functions.

Inclusion of semi-inclusive DIS data for the first time.

Neural network parametrization enables flexible, unbiased fits.

Abstract

We present MAPFF1.0_Lambda, the first global analysis at next-to-next-to-leading order in perturbative QCD of the collinear unpolarised fragmentation functions of Lambda hyperons. The fit is based on data from single-inclusive electron-positron annihilation, and from both neutral-current and -- for the first time -- charged-current semi-inclusive deep-inelastic scattering. We have adopted a statistical framework based on Monte Carlo sampling and parametrised fragmentation functions in terms of a neural network. The fragmentation function set comprises a total of seven independent parton flavours, allowing for the first independent determination of valence-quark distributions. Our analysis offers new insights into the hadronisation mechanism of strange baryons and establishes a baseline for future phenomenological and experimental investigations.