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  • Letter
  • Open Access

Machine learning holographic black hole from lattice QCD equation of state

Xun Chen†

Mei Huang*

  • School of Nuclear Science and Technology, University of South China, Hengyang 421001, China

  • School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

  • *Corresponding author: huangmei@ucas.ac.cn
  • †chenxun@usc.edu.cn
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Phys. Rev. D 109, L051902 – Published 25 March, 2024

DOI: https://doi.org/10.1103/PhysRevD.109.L051902

Abstract

Based on lattice QCD results of equation of state and baryon number susceptibility at zero baryon chemical potential, and supplemented by machine learning techniques, we construct the analytic form of the holographic black hole metric in the Einstein-Maxwell-Dilaton framework for pure gluon, 2-flavor, and (2+1)-flavor systems, respectively. The dilaton potentials solved from Einstein equations are in good agreement with the extended nonconformal DeWolfe-Gubser-Rosen type dilaton potentials fixed by lattice QCD equation of state, which indicates the robustness of the Einstein-Maxwell-Dilaton framework. The predicted critical end point in the (2+1)-flavor system is located at (Tc=0.094  GeV, μBc=0.74  GeV), which is close to the results from the realistic Polyakov-Nambu-Jona-Lasinio model, the functional renormalization group, and the holographic model with extended DeWolfe-Gubser-Rosen dilaton potential.

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Outline

Information

Phys. Rev. D 109, L051902– Published 25 March, 2024
  • Received 28 January 2024
  • Accepted 4 March 2024
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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