An equation-of-state-meter of quantum chromodynamics transition from deep learning.

Area of Science:

• Nuclear Physics
• High-Energy Physics
• Cosmology

Background:

• The early universe contained a primordial state of matter with free quarks and gluons.
• High-energy heavy-ion collisions aim to recreate this quark-gluon plasma.
• Understanding the equation of state (EoS) of this matter is key to unlocking early universe physics.

Purpose of the Study:

• To develop a method for identifying the equation of state (EoS) in heavy-ion collision simulations.
• To utilize deep learning for analyzing the properties of quark-gluon plasma.
• To create a model-independent tool for studying the quantum chromodynamics phase transition.

Main Methods:

• Employed supervised learning with a deep convolutional neural network.
• Analyzed high-level correlations in particle spectra (transverse momentum and azimuthal angle).
• Developed an effective 'EoS-meter' for hydrodynamic simulations.

Main Results:

• The deep convolutional neural network successfully identified the equation of state (EoS).
• The learned correlations serve as a robust indicator of the EoS.
• The method proved model-independent and insensitive to initial simulation conditions.

Conclusions:

• Deep learning offers a powerful approach to determine the equation of state (EoS) in heavy-ion collisions.
• The developed EoS-meter provides a novel way to probe the quantum chromodynamics phase transition.
• This technique advances our understanding of primordial matter and the early universe.