Flavor dependent Critical endpoint from holographic QCD through machine learning
Xun Chen, Mei Huang

TL;DR
This paper uses holographic QCD models combined with machine learning to explore the QCD phase diagram, identifying critical endpoints and phase transition types for various flavor systems at finite temperature and chemical potential.
Contribution
It introduces a novel approach integrating lattice QCD data with holographic models and machine learning to determine phase transition characteristics and critical points across different flavor configurations.
Findings
Deconfinement transition is first order for pure gluon system at T_c=0.265 GeV.
Critical endpoints are identified for 2-flavor, 2+1-flavor, and 2+1+1-flavor systems.
Differences between flavor systems become significant at high chemical potential.
Abstract
QCD phase diagram in the plane and the equation of state for pure gluon, 2-flavor, 2+1-flavor systems, and 2+1+1-flavor systems have been investigated using the Einstein-Maxwell-Dilaton (EMD) framework at finite temperature and chemical potential. By inputting lattice QCD data for the equation of state and baryon number susceptibility at zero chemical potential into holographic model, all the parameters can be determined with the aid of machine learning algorithms. Our findings indicate that the deconfinement phase transition is of first order for the pure gluon system with critical temperature GeV at vanishing chemical potential. The phase transition for the 2-flavor, 2+1-flavor systems, and 2+1+1-flavor systems are crossover at vanishing chemical potential and first-order at high chemical potential, and the critical endpoint (CEP) in the plane locates…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Quantum Chromodynamics and Particle Interactions · High-Energy Particle Collisions Research
