Machine learning driven identification of heavy flavor decay leptons in proton-proton collisions at the Large Hadron Collider

Abstract

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 $\sqrt{s}=13.6$~TeV. We use \DCAXY, \DCAZ~and pseudo-rapidity 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.