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Machine learning holographic black hole from lattice QCD equation of state

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成果类型:
期刊论文
作者:
Chen, Xun;Huang, Mei
通讯作者:
Huang, M
作者机构:
[Chen, Xun] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
[Huang, Mei; Huang, M] Univ Chinese Acad Sci, Sch Nucl Sci & Technol, Beijing 100049, Peoples R China.
通讯机构:
[Huang, M ] U
Univ Chinese Acad Sci, Sch Nucl Sci & Technol, Beijing 100049, Peoples R China.
语种:
英文
期刊:
PHYSICAL REVIEW D
ISSN:
2470-0010
年:
2024
卷:
109
期:
5
页码:
L051902
基金类别:
National Natural Science Foundation of China (NSFC) [12235016, 12221005, 12147150]; Strategic Priority Research Program of Chinese Academy of Sciences [XDB34030000]; Research Foundation of Education Bureau of Hunan Province, China [21B0402]; Natural Science Foundation of Hunan Province of China [2022JJ40344]
机构署名:
本校为第一机构
院系归属:
核科学技术学院
摘要:
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 ...

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