## “School of Particles And Accelerator”

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Paper   IPM / Particles And Accelerator / 17032
School of Particles and Accelerator
Title:   Fragmentation Functions for $\Xi ^-/\bar{\Xi}^+$ Using Neural Networks
Author(s):
 1 Maryam Soleymaninia 2 Hadi Hashamipour 3 Hamzeh Khanpour 4 Hubert Spiesberger
Status:   Published
Journal: Nucl. Phys. A
No.:  122564
Vol.:  1029
Year:  2022
Supported by:  IPM
Abstract:
We present a determination of fragmentation functions (FFs) for the octet baryon $\Xi ^-/\bar{\Xi}^+$ from data for single inclusive electron-positron annihilation. Our parametrization in this QCD analysis is provided in terms of a Neural Network (NN). We determine fragmentation functions for $\Xi ^-/\bar{\Xi}^+$ at next-to-leading order and for the first time at next-to-next-to-leading order in perturbative QCD. We discuss the improvement of higher-order QCD corrections, the quality of fit, and the comparison of our theoretical results with the fitted datasets. As an application of our new set of fragmentation functions, named {\tt SHKS22}, we present predictions for $\Xi ^- / \bar{\Xi}^+$ baryon production in proton-proton collisions at the LHC experiments.