Jet Topics: Disentangling Quarks and Gluons at Colliders

Area of Science:

• High Energy Physics
• Particle Physics
• Machine Learning Applications

Background:

• Identifying particle jets in collider data is crucial for understanding fundamental physics.
• Traditional methods often rely on simulations or theoretical models, which can introduce biases.
• A data-driven approach is needed to classify jets more robustly.

Purpose of the Study:

• To introduce a novel framework called "jet topics" for classifying particle jets.
• To leverage topic modeling techniques from natural language processing for jet physics.
• To demonstrate the ability to extract jet properties with minimal reliance on simulations or theory.

Main Methods:

• Applied topic modeling techniques to distributions of observables in collider jet data.
• Utilized parton shower samples for a proof-of-concept demonstration.
• Analyzed multidifferential cross sections at the hadron level.

Main Results:

• Successfully extracted separate quark and gluon jet distributions for constituent multiplicity.
• Determined distinct quark and gluon jet rapidity spectra from a mixed Z-plus-jet sample.
• Showcased the framework's ability to work directly with experimental data.

Conclusions:

• "Jet topics" provides a powerful, data-driven method for classifying particle jets.
• The framework has potential implications for defining quark and gluon jets beyond leading-logarithmic accuracy.
• This approach is expected to be valuable for various analyses at the Large Hadron Collider.