Inclusive, prompt and nonprompt 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 anticharm quarks ( c c ¯ ), 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 ( p p ) 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 p p collisions at s = 7 and 13 TeV simulated using 8. 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 nonprompt J / ψ , its multiplicity dependence, and the p T dependence of fraction of nonprompt J / ψ ( f B ) are shown. These results are compared to experimental findings wherever possible.