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

Studies related to J/ψ meson, a bound state of charm and anti-charm quarks (cc̅), in heavy-ion collisions, provide genuine testing grounds for the theory of strong interaction, quantum chromodynamics (QCD). 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 non-prompt productions. The prompt J/ψ is produced directly from the hadronic interactions or via feed-down from directly produced higher charmonium states, whereas non-prompt 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 non-prompt 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 (p_ T) and rapidity (y) differential measurements of inclusive, prompt, and non-prompt J/ψ, its multiplicity dependence, and the p_ T dependence of fraction of non-prompt J/ψ (f_ B) are shown. These results are compared to experimental findings wherever possible.