2025/08/31 by Sahoo, Raghunath, Goswami, Kangkan, Prasad, Suraj
#FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #High Energy Physics - Theory (hep-th) #Nuclear Theory (nucl-th)
paper · doi:10.48550/arxiv.2509.00712
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 semi-leptonic 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 semi-leptonic 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 √(s)=13.6~TeV. We use DCA, pseudorapidity, and transverse momentum as the input to the machine. The ML model provides an accuracy of 98% for heavy-flavor decay electrons and almost 100% for heavy-flavor decay muons.