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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.11855 |
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| _version_ | 1866912587661180928 |
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| author | Mahaut, Virgile Polano, Luca Bacchetta, Alessandro Bertone, Valerio Cerutti, Matteo Radici, Marco Rossi, Lorenzo |
| author_facet | Mahaut, Virgile Polano, Luca Bacchetta, Alessandro Bertone, Valerio Cerutti, Matteo Radici, Marco Rossi, Lorenzo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11855 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extraction of Dihadron Fragmentation Functions at NNLO with and without Neural Networks Mahaut, Virgile Polano, Luca Bacchetta, Alessandro Bertone, Valerio Cerutti, Matteo Radici, Marco Rossi, Lorenzo High Energy Physics - Phenomenology 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. |
| title | Extraction of Dihadron Fragmentation Functions at NNLO with and without Neural Networks |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2509.11855 |