Inclusive, prompt and nonprompt J/ψ identification in proton-proton collisions at the Large Hadron Collider using machine learning

Abstract

Studies related to J/ψ meson, a bound state of charm and anticharm quarks (cc¯), in heavy-ion collisions, provide genuine testing grounds for the theory of strong interaction, quantum chromodynamics. To better understand the underlying production mechanism, cold nuclear matter effects, and influence from the quark-gluon plasma, baseline measurements are also performed in proton-proton (pp) and proton-nucleus (p-A) collisions. The inclusive J/ψ measurement has contributions from both prompt and nonprompt productions. The prompt J/ψ is produced directly from the hadronic interactions or via feed down from directly produced higher charmonium states, whereas nonprompt J/ψ comes from the decay of beauty hadrons. In experiments, J/ψ is reconstructed through its electromagnetic decays to lepton pairs, in either e++e- or μ++μ- decay channels. In this work, for the first time, machine learning techniques are implemented to separate the prompt and nonprompt dimuon pairs from the background to obtain a better identification of the J/ψ signal for different production modes. The study has been performed in pp collisions at s=7 and 13 TeV simulated using pythia8. Machine learning models such as XGBoost and LightGBM are explored. The models could achieve up to 99% prediction accuracy. The transverse momentum (pT) and rapidity (y) differential measurements of inclusive, prompt, and nonprompt J/ψ, its multiplicity dependence, and the pT dependence of fraction of nonprompt J/ψ (fB) are shown. These results are compared to experimental findings wherever possible. © 2024 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.