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Studies related to $\rm{J}/\psi$ meson, a bound state of charm and anti-charmquarks ($c\bar{c}$), in heavy-ion collisions, provide genuine testing groundsfor the theory of strong interaction, quantum chromodynamics (QCD). To betterunderstand the underlying production mechanism, cold nuclear matter effects,and influence from the quark-gluon plasma, baseline measurements are alsoperformed in proton-proton ($pp$) and proton-nucleus ($p$--A) collisions. Theinclusive $\rm{J}/\psi$ measurement has contributions from both prompt andnon-prompt productions. The prompt $\rm{J}/\psi$ is produced directly from thehadronic interactions or via feed-down from directly produced higher charmoniumstates, whereas non-prompt $\rm{J}/\psi$ comes from the decay of beautyhadrons. In experiments, $\rm{J}/\psi$ is reconstructed through itselectromagnetic decays to lepton pairs, in either $e^{+}+e^{-}$ or$\mu^{+}+\mu^{-}$ decay channels. In this work, for the first time, machinelearning techniques are implemented to separate the prompt and non-promptdimuon pairs from the background to obtain a better identification of the$\rm{J}/\psi$ signal for different production modes. The study has beenperformed in $pp$ collisions at $\sqrt{s} = 7$ and 13 TeV simulated usingPYTHIA8. Machine learning models such as XGBoost and LightGBM are explored. Themodels could achieve up to 99\% prediction accuracy. The transverse momentum($p_{\rm T}$) and rapidity ($y$) differential measurements of inclusive,prompt, and non-prompt $\rm{J}/\psi$, its multiplicity dependence, and the$p_{\rm T}$ dependence of fraction of non-prompt $\rm{J}/\psi$ ($f_{\rm B}$)are shown. These results are compared to experimental findings whereverpossible.

Inclusive, prompt and non-prompt $\rm{J}/\psi$ identification in proton-proton collisions at the Large Hadron Collider using machine learning