  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 in the quark-gluon plasma 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. Corroborating past
results, I demonstrate the capability of a fully data-driven technique called
topic modeling in separating quark and gluon contributions to jet observables.
The data-driven topic separation results can further be used to extract jet
substructures, such as jet shapes and jet fragmentation function, and their
respective QGP modifications. In addition, I propose the use of machine
learning constructed observables and demonstrate the potential to increase
separability for the input observable. 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, their substructures, and their modification in the QGP.
