The study of heavy-flavor hadrons is topical in the era of precision measurements, which is useful to test theories based on pQCD. The heavy-flavor hadrons are produced initially during heavy-ion or hadronic collisions and are one of the best probes to understand the initial stages of the collisions as well as the system evolution. In experiments, the heavy-flavor sectors are studied directly via their decay to different hadrons or di-leptons or via their semileptonic decay, which is accompanied by additional neutrinos. However, their measurement in experiments is resource intensive and requires input from different Monte Carlo event generators. In this study, we provide an independent method based on machine learning algorithms to separate such leptons coming from heavy-flavor semileptonic decays. We use pythia8 to generate events for this study, which gives a good qualitative and quantitative description of heavy-flavor production in $pp$ collisions. We use the xgboost model for this study, which is trained with $pp$ collisions at $\sqrt{s}=13.6\mathrm{TeV}$. We use ${\mathrm{DCA}}_{\mathrm{xy}}$, ${\mathrm{DCA}}_{\mathrm{Z}}$, and pseudorapidity as the input features to the machine. The ML model provides an accuracy of 98% for heavy-flavor decay electrons and almost 100% for heavy-flavor decay muons.

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• Quark-gluon plasma
• Relativistic heavy-ion collisions
• Strong interaction