Erlangen 2026 – scientific programme
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HK: Fachverband Physik der Hadronen und Kerne
HK 40: Heavy-Ion Collisions and QCD Phases VI
HK 40.1: Talk
Thursday, March 19, 2026, 16:15–16:30, PHIL C 601
Machine-learning-based modeling of particle production in pp collisions measured by ALICE — •Maria Calmon Behling, Mario Krüger, Jerome Jung, and Henner Büsching — Institut für Kernphysik, Goethe Universität Frankfurt
During the data-taking campaigns Run 1 and Run 2 at the LHC, the ALICE collaboration recorded a large amount of proton-proton (pp) collisions across a variety of center-of-mass energies (√s ). This dataset is well suited to study the energy dependence of particle production. Deep neural networks (DNNs) provide a data-driven approach to capture the multidimensional dependence of particle production on fundamental observables like the charged-particle multiplicity (Nch), the transverse momentum (pT) and √s .
In this talk, ALICE measurements of Nch- and pT-dependent inclusive charged-particle spectra at various center-of-mass energies are parametrized with DNNs. Together with a DNN-based particle composition, this is used to provide particle-differential predictions for a wide range of energies. The DNN predictions are compared to existing measurements as well as to commonly used event generators. The results allow estimating the transverse energy of the final-state particles, which is directly related to the initial energy density of the collisions.
Supported by BMFTR and the Helmholtz Association.
Keywords: ALICE; LHC; pp collisions; charged particles; machine learning