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Quantifying vacuum-like jets in heavy-ion collisions: a machine learning study.

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Researchers developed a Transformer classifier to identify modified jets in nucleus-nucleus collisions. This method distinguishes jets interacting with quark-gluon plasma, overcoming limitations in studying jet modification mechanisms.

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Area of Science:

  • High Energy Physics
  • Quantum Chromodynamics
  • Particle Physics

Background:

  • Jet modification by quark-gluon plasma (QGP) is studied by comparing nucleus-nucleus (AA) and proton-proton (pp) collisions.
  • Vacuum-like jets in AA collisions dilute observed modifications, hindering study of underlying physics.
  • Identifying individual jet modification is crucial for detailed analysis.

Purpose of the Study:

  • To develop a method for on a jet-by-jet basis identification of jet modification by QGP.
  • To overcome limitations posed by jet dilution in AA collisions.
  • To study the underlying physical mechanisms of jet-medium interaction.

Main Methods:

  • A Transformer classifier was trained on low-level jet constituents (4-momenta).
  • The classifier utilized information inaccessible to methods using high-level observables.
  • Medium response and underlying event contamination were accounted for.

Main Results:

  • The Transformer successfully identified unequivocally modified jets in AA collisions.
  • The classifier captured discriminating information beyond traditional methods.
  • An upper bound was estimated for the fraction of jets indistinguishable from pp jets.

Conclusions:

  • Transformer-based classification enables precise identification of modified jets.
  • This approach facilitates detailed studies of jet-medium interactions.
  • The method provides a new tool for analyzing QGP effects on jets.