João Arruda Gonçalves

arXiv2025

Quantifying vacuum-like jets in heavy-ion collisions: a Machine Learning study

The modification of jets by interaction with the Quark Gluon Plasma has been extensively established through the comparison of observables computed for samples of jets produced in nucleus-nucleus collisions and proton-proton collisions. The presence of vacuum-like jets, jets that experienced little interaction with the Quark Gluon Plasma, in the nucleus-nucleus samples dilutes the overall observed modification hindering the detailed study of the underlying physical mechanisms. The ability to ascertain on a jet-by-jet basis the degree of modification of a jet would be an invaluable step in overcoming this limitation. We consider a Transformer classifier, trained on a low-level representation of jets given by the 4-momenta of all its constituents. We show that the Transformer is able to capture discriminating information not accessible to other architectures which use high-level physical observables as input. The Transformer allows us to identify, in the experimentally relevant case where both medium response and underlying event contamination are accounted for, a class of jets that have been unequivocally modified. Further, we perform a robust estimate of the upper bound for the fraction of jets in nucleus-nucleus collisions that are, for all purposes, indistinguishable from those produced in proton-proton collisions.

hep-ph↗

arXiv2025

Apples to Apples in Jet Quenching: robustness of Machine Learning classification of quenched jets to Underlying Event contamination

Progress in the theoretical understanding of parton branching dynamics within an expanding Quark Gluon Plasma relies on detailed and fair comparisons with experimental data for reconstructed jets. Such comparisons are only meaningful when the computed jet, be it analytically or via event generation, accounts for the complexity of jets reconstructed in the challenging environment of heavy-ion collisions. Jet reconstruction in heavy ion collisions involves a necessarily imperfect subtraction of the large and fluctuating underlying event: reconstructed jets always include underlying event contamination. To identify true jet quenching effects, modifications due to the interaction of the branching partonic system with the Quark Gluon Plasma, we establish a baseline that accounts for possible background contamination on unmodified jets. In practical terms, jet quenching effects are only those not present in jets produced in proton-proton collisions that have been embedded in a realistic heavy-ion background and where subtraction has been carried out analogously to that in the heavy ion case. With this setup, we assess the sensitivity to underlying event of commonly discussed jet quenching observables and its impact on the robustness of Machine Learning studies, aimed at classifying jets according to their degree of modification by the Quark Gluon Plasma, that rely on those observables. We find the discrimination power of a simple Boosted Decision Tree to be robust in the realistic scenario where both medium response and underlying event are present, giving support to portability to the experimental context.

hep-ph↗