Feasibility of Tagging Heavy Flavor Jets at RHIC With Machine Learning

The properties of the Quark-Gluon Plasma (QGP), a hot and dense medium made up of deconfined quarks and gluons (partons), can be studied through ultrarelativistic heavy-ion collisions. In the early stages of the collisions, high energy partons are created, which fragment into sprays of hadrons, called jets. Jets are used to probe the entire evolution of the QGP that they traverse. Classifying jets based on the flavor of the parton that initiated them as heavy or light is a fundamental tool for studying the properties of the QGP as different flavors undergo different levels of collisional and radiative energy losses. It has been shown that it is possible to classify (tag) them at LHC energies (√s = 7 TeV). However, the ratio of production cross sections of light to heavy flavor jets at RHIC energies (√s = 200 GeV) is very high. Therefore, we have developed an artificial neural network that maintains the efficiency while minimizing the misidentification probability of tagging heavy flavor jets at RHIC. We have also developed a new jet tagging strategy, where we only consider jets with at least one high transverse momentum lepton constituent. This selection lowers the ratio of light to heavy flavor jets, increasing the efficiency of tagging heavy flavor jets.