Jet Tomography in Heavy-Ion Collisions with Deep Learning

Area of Science:

• High Energy Physics
• Quantum Chromodynamics (QCD)
• Deep Learning Applications

Background:

• High-energy jets traversing deconfined QCD matter undergo modifications.
• Studying jets based on their final energy introduces selection biases.
• Understanding initial jet properties is crucial for probing the quark-gluon plasma.

Purpose of the Study:

• To develop a deep learning technique for quantifying jet modifications on a jet-by-jet basis.
• To enable the study of jets based on their initial energy, bypassing final-state interaction biases.
• To utilize jets as tomographic probes of the quark-gluon plasma.

Main Methods:

• Application of deep learning models to identify the degree of modification of high-energy jets.
• Analysis of jet configuration profiles in the transverse plane, considering production point and orientation.
• Removal of selection biases induced by final-state interactions.

Main Results:

• Deep learning accurately identifies jet modifications, allowing study of initial jet energy.
• The technique provides unique access to jet configuration profiles and orientation.
• Precise localization of dijet pair production points within the nuclear overlap region.

Conclusions:

• This novel deep learning method offers a new way to study jet properties in high-energy nuclear collisions.
• It overcomes limitations of previous methods by focusing on initial jet energy.
• The technique is a significant advancement in using jets as tomographic probes of the quark-gluon plasma.