Quark-Gluon Discrimination at the Large Hadron Collider

Quarks and gluons are part of the Standard Model of particle physics, but cannot be observed directly in high energy physics experiments since they appear as a shower of hadrons called jets. Analyses of jets that includes their substructure is a recent development that holds the promise of being able to differentiate between gluon jets and quark jets. Since quark jets are the main focus for many new physics searches, reduction of background from gluon jets increases the sensitivity to new physics signatures. In order to distinguish between quark and gluon jets, we create a Quark-Gluon Likelihood (QGL) discriminant and apply it to jets from simulated new physics signals as well as background. The QGL discriminator uses neural networks, which take input variables such as the primary vertices, transverse momenta of jets, and other parameters related to the jet substructure to calculate the QGL discriminator values. We show preliminary results showing improvement in signal sensitivity obtained from simulated new physics samples of supersymmetric gluino and SM background consisting of multiple jets.