DNN predictions for pp reference $p_\mathrm{T}$ spectra at unmeasured $\sqrt{s}$
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866916005573296128 |
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| author | Behling, Maria A. Calmon Krüger, Mario Jung, Jerome Büsching, Henner |
| author_facet | Behling, Maria A. Calmon Krüger, Mario Jung, Jerome Büsching, Henner |
| contents | Studies of the properties of the Quark-Gluon Plasma in high-energy heavy-ion collisions commonly facilitate proton-proton (pp) collisions at the same center-of-mass energy per nucleon pair as a reference measurement. In this paper, a deep neural network-based approach for interpolating and extrapolating pp reference transverse-momentum spectra to unmeasured energies is presented. The model is trained with ALICE data from LHC Runs 1 and 2 and provides predictions for center-of-mass energies relevant to LHC Run 3 and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12490 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DNN predictions for pp reference $p_\mathrm{T}$ spectra at unmeasured $\sqrt{s}$ Behling, Maria A. Calmon Krüger, Mario Jung, Jerome Büsching, Henner High Energy Physics - Experiment Studies of the properties of the Quark-Gluon Plasma in high-energy heavy-ion collisions commonly facilitate proton-proton (pp) collisions at the same center-of-mass energy per nucleon pair as a reference measurement. In this paper, a deep neural network-based approach for interpolating and extrapolating pp reference transverse-momentum spectra to unmeasured energies is presented. The model is trained with ALICE data from LHC Runs 1 and 2 and provides predictions for center-of-mass energies relevant to LHC Run 3 and beyond. |
| title | DNN predictions for pp reference $p_\mathrm{T}$ spectra at unmeasured $\sqrt{s}$ |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2605.12490 |