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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
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
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.

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