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<p>We present a new extraction of unpolarized Dihadron Fragmentation Functions, which describe the probability density for an unpolarized parton to fragment into a <i>π</i><sup>+</sup><i>π</i><sup><i>−</i></sup> pair. Our analysis is based on data from the BELLE collaboration. We improve on previous determinations in several key aspects: we employ state-of-the-art perturbative QCD calculations up to next-to-next-to-leading order (NNLO); we limit the use of Monte Carlo event generators to estimating the relative contributions of different flavors, a necessary input due to the limited flavor sensitivity of the available data; and, in addition to a traditional fit based on a physics-informed functional form, we explore a Neural Network parametrization. This latter approach paves the way for more robust and flexible determinations of Dihadron Fragmentation Functions using machine learning techniques.</p>

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Extraction of dihadron fragmentation functions at NNLO with and without neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Virgile Mahaut,
  • Luca Polano,
  • Alessandro Bacchetta,
  • Valerio Bertone,
  • Matteo Cerutti,
  • Marco Radici,
  • Lorenzo Rossi

摘要

We present a new extraction of unpolarized Dihadron Fragmentation Functions, which describe the probability density for an unpolarized parton to fragment into a π+π− pair. Our analysis is based on data from the BELLE collaboration. We improve on previous determinations in several key aspects: we employ state-of-the-art perturbative QCD calculations up to next-to-next-to-leading order (NNLO); we limit the use of Monte Carlo event generators to estimating the relative contributions of different flavors, a necessary input due to the limited flavor sensitivity of the available data; and, in addition to a traditional fit based on a physics-informed functional form, we explore a Neural Network parametrization. This latter approach paves the way for more robust and flexible determinations of Dihadron Fragmentation Functions using machine learning techniques.