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Autori principali: Behling, Maria A. Calmon, Krüger, Mario, Jung, Jerome, Büsching, Henner
Natura: Preprint
Pubblicazione: 2026
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Accesso online:https://arxiv.org/abs/2605.12490
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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