Data-driven extraction of the substructure of quark and gluon jets in proton-proton and heavy-ion collisions

The modification of quark- and gluon-initiated jets in the quark-gluon plasma produced in heavy-ion collisions is a long-standing question that has not yet received a definitive answer from experiments. In particular, the size of the modifications differs between theoretical models. Therefore a fully data-driven technique is crucial for an unbiased extraction of the quark and gluon jet spectra and substructure. In this talk we demonstrate a fully data-driven method for separating quark and gluon contributions to jet observables using a statistical technique called topic modeling. In addition to variables such as constituent multiplicity, we employ machine learning based algorithm to enhance the separation power of the quark and gluon like jets. We will also demonstrate that jet substructures, such as jet shapes and jet fragmentation function, could be extracted using this data-driven method. This proof-of-concept study is based on proton-proton and heavy-ion collision events from the PYQUEN generator with statistics accessible in Run 4 of the Large Hadron Collider. These results suggest the potential for an experimental determination of quark- and gluon-jet spectra and their substructures.