Machine learning holographic black hole from lattice QCD equation of state
Abstract: Based on lattice QCD results of equation of state (EOS) 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 (EMD) 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 non-conformal DeWolfe-Gubser-Rosen (DGR) type dilaton potentials fixed by lattice QCD EOS, which indicates the robustness of the EMD framework. The predicted critical endpoint (CEP) in the 2+1-flavor system is located at $(Tc$=0.094GeV, $\muc_B$=0.74GeV), which is close to the results from the realistic Polyakov-Nambu-Jona-Lasinio(PNJL) model, the functional renormalization group, and the holographic model with extended DeWolfe-Gubser-Rosen dilaton potential.
- R. D. Pisarski and F. Wilczek, Phys. Rev. D 29, 338 (1984).
- Y. Hatta and T. Ikeda, Phys. Rev. D 67, 014028 (2003), arXiv:hep-ph/0210284 .
- Y. Hatta and M. A. Stephanov, Phys. Rev. Lett. 91, 102003 (2003), [Erratum: Phys.Rev.Lett. 91, 129901 (2003)], arXiv:hep-ph/0302002 .
- M. M. Aggarwal et al. (STAR), Phys. Rev. Lett. 105, 022302 (2010a), arXiv:1004.4959 [nucl-ex] .
- M. M. Aggarwal et al. (STAR) (2010) arXiv:1007.2613 [nucl-ex] .
- L. Adamczyk et al. (STAR), Phys. Rev. Lett. 112, 032302 (2014), arXiv:1309.5681 [nucl-ex] .
- X. Luo and N. Xu, Nucl. Sci. Tech. 28, 112 (2017), arXiv:1701.02105 [nucl-ex] .
- J. Adam et al. (STAR), Phys. Rev. Lett. 126, 092301 (2021), arXiv:2001.02852 [nucl-ex] .
- M. Abdallah et al. (STAR), Phys. Rev. C 107, 024908 (2023), arXiv:2209.11940 [nucl-ex] .
- Y. Nambu and G. Jona-Lasinio, Phys. Rev. 122, 345 (1961a).
- Y. Nambu and G. Jona-Lasinio, Phys. Rev. 124, 246 (1961b).
- F. Gao and Y.-x. Liu, Phys. Rev. D 94, 076009 (2016), arXiv:1607.01675 [hep-ph] .
- J. M. Maldacena, Adv. Theor. Math. Phys. 2, 231 (1998), arXiv:hep-th/9711200 .
- S. S. Gubser and A. Nellore, Phys. Rev. D 78, 086007 (2008), arXiv:0804.0434 [hep-th] .
- M. Jarvinen, EPJ Web Conf. 274, 08006 (2022), arXiv:2211.10005 [hep-ph] .
- Y. Yang and P.-H. Yuan, JHEP 11, 149 (2014), arXiv:1406.1865 [hep-th] .
- Y. Yang and P.-H. Yuan, JHEP 12, 161 (2015), arXiv:1506.05930 [hep-th] .
- D. Dudal and S. Mahapatra, Phys. Rev. D 96, 126010 (2017), arXiv:1708.06995 [hep-th] .
- D. Dudal and S. Mahapatra, JHEP 07, 120 (2018), arXiv:1805.02938 [hep-th] .
- L. Zhang and M. Huang, Phys. Rev. D 106, 096028 (2022), arXiv:2209.00766 [nucl-th] .
- A. Bazavov et al. (HotQCD), Phys. Rev. D 90, 094503 (2014), arXiv:1407.6387 [hep-lat] .
- A. Bazavov et al., Phys. Rev. D 95, 054504 (2017), arXiv:1701.04325 [hep-lat] .
- F. Gao and J. M. Pawlowski, Phys. Rev. D 102, 034027 (2020), arXiv:2002.07500 [hep-ph] .
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