Quantifying vacuum-like jets in heavy-ion collisions: a Machine Learning study
Miguel Crispim Rom\~ao, Jo\~ao Arruda Gon\c{c}alves, Jos\'e Guilherme Milhano

TL;DR
This study employs a Transformer-based machine learning model to identify vacuum-like jets in heavy-ion collisions, enabling more precise analysis of jet modifications caused by the Quark Gluon Plasma.
Contribution
The paper introduces a Transformer classifier trained on low-level jet data, capable of distinguishing minimally modified jets from those affected by the medium, surpassing traditional high-level observable methods.
Findings
Transformer accurately identifies vacuum-like jets in complex environments
Estimates the upper bound of unmodified jets in nucleus-nucleus collisions
Demonstrates improved discrimination over existing high-level observable approaches
Abstract
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…
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Particle physics theoretical and experimental studies · Particle Detector Development and Performance
