Discriminating QCD Compton and Quark-Antiquark Annihilation Processes in $\gamma$ + Jets Using Interpretable Machine Learning

Abstract

We investigate how effectively final-state jet substructure can discriminate between QCD Compton and quark-antiquark annihilation processes from photon-jet production in $pp$ collisions at $\sqrt{s}=13$ TeV. Using infrared- and collinear-safe jet observables, multivariate classifiers -- boosted decision trees and multilayer perceptrons -- are trained on labeled quark- and gluon-initiated jets from dijet events and applied to photon-jet samples. Observables probing soft and wide-angle radiation, in particular jet multiplicity and jet girth, dominate the discrimination. The jet mass provides a complementary but weaker contribution, while the jet charge exhibits negligible discriminating power. A comparison of the two classifiers demonstrates that the achievable separation is limited primarily by QCD radiation effects rather than by classifier complexity. These findings quantify the extent to which information about the underlying hard process survives hadronization and realistic jet reconstruction, providing a physics-driven baseline for precision jet measurements in $pp$, $ep/$A, and heavy-ion collisions.