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HK: Fachverband Physik der Hadronen und Kerne
HK 33: Heavy-Ion Collisions and QCD Phases VII
HK 33.2: Talk
Tuesday, March 12, 2024, 18:00–18:15, HBR 62: EG 05
Modeling charged-particle spectra of pp collisions with deep neural networks — •Maria A. Calmon Behling — Institut für Kernphysik, Goethe-Universität Frankfurt
During the data-taking campaigns Run 1 and Run 2 of the Large Hadron Collider (LHC), the ALICE collaboration collected a large amount of proton-proton (pp) collisions across a variety of center-of-mass energies (√s ). This extensive dataset is well suited to study the energy dependence of particle production. Deep neural networks (DNNs) provide a powerful regression tool to capture underlying multidimensional correlations inherent in the data. In this contribution, DNNs are used to parameterize recent ALICE measurements of charged-particle multiplicity (Nch) distributions and transverse momentum (pT) spectra. These observables are predicted by means of an ensemble method, extrapolating the measurements towards higher Nch and pT values as well as to unmeasured √s from 0.5 to 100 TeV. We demonstrate that the predicted pT spectra can serve as a reference for future heavy-ion measurements, e.g. the O–O campaign planned in LHC Run 3, where no dedicated pp data-taking at the same √s is currently foreseen.
Supported by BMBF and the Helmholtz Association.
Keywords: pp collisions; LHC; ALICE; neural networks; charged particles