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

TL;DR

This paper introduces a data-driven method using topic modeling to separate and analyze quark and gluon jet substructures in proton-proton and heavy-ion collisions, aiming to clarify their modifications in the quark-gluon plasma.

Contribution

It presents a novel application of topic modeling to extract quark and gluon jet substructures from collision data, demonstrating robustness and potential for experimental use.

Findings

Jet substructure observables can be extracted using the method.

The approach is robust against background fluctuations.

Potential for experimental determination of jet modifications.

Abstract

The different modifications of quark- and gluon-initiated jets in the quark-gluon plasma (QGP) produced in heavy-ion collisions is a long-standing question that has not yet received a definitive answer from experiments. In particular, the relative sizes of the modification of quark and gluon jets differ between theoretical models. Therefore, a fully data-driven technique is crucial for an unbiased extraction of the quark and gluon jet spectra and substructure. We perform a proof-of-concept study based on proton-proton and heavy-ion collision events from the \textsc{Pyquen} generator with statistics accessible in Run 4 of the Large Hadron Collider. We use a statistical technique called topic modeling to separate quark and gluon contributions to jet observables. We demonstrate that jet substructure observables, such as the jet shape and jet fragmentation function, can be extracted using this data-driven method. These values can then be used to obtain the modification of quark and gluon jet substructures in the QGP. We also perform the topic separation on smeared input data to demonstrate that the approach is robust to fluctuations arising from a QGP background. These results suggest the potential for an experimental determination of quark and gluon jet spectra and their substructure.