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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06729 |
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| _version_ | 1866915655254540288 |
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| author | Chen, Xun Chen, Yidian Zhou, Kai |
| author_facet | Chen, Xun Chen, Yidian Zhou, Kai |
| contents | We develop a machine learning assisted holographic model that consistently describes both the equation of state and glueball spectrum of pure Yang-Mills theory, achieved through neural network reconstruction of Einstein-dilaton gravity. Our framework incorporates key non-perturbative constraints of lattice QCD data: the ground ($0^{++}$) and first-excited ($0^{++*}$) scalar glueball masses pins down the infrared (IR) geometry, while entropy density data anchors the ultraviolet (UV) behavior of the metric. A multi-stage neural network optimization then yields the full gravitational dual -- warp factor $A(z)$ and dilaton field $Φ(z)$ -- that satisfies both spectroscopic and thermodynamic constraints. The resulting model accurately reproduces the deconfinement phase transition thermodynamics (pressure, energy density, trace anomaly) and predicts higher glueball excitations ($0^{++**}$, $0^{++***}$) consistent with available lattice calculations. This work establishes a new paradigm for data-driven holographic reconstruction, solving the long-standing challenge of unified description of confinement thermodynamics and spectroscopy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Einstein-Dilaton Model for Pure Yang-Mills Thermodynamics and Glueball Spectrum Chen, Xun Chen, Yidian Zhou, Kai High Energy Physics - Phenomenology We develop a machine learning assisted holographic model that consistently describes both the equation of state and glueball spectrum of pure Yang-Mills theory, achieved through neural network reconstruction of Einstein-dilaton gravity. Our framework incorporates key non-perturbative constraints of lattice QCD data: the ground ($0^{++}$) and first-excited ($0^{++*}$) scalar glueball masses pins down the infrared (IR) geometry, while entropy density data anchors the ultraviolet (UV) behavior of the metric. A multi-stage neural network optimization then yields the full gravitational dual -- warp factor $A(z)$ and dilaton field $Φ(z)$ -- that satisfies both spectroscopic and thermodynamic constraints. The resulting model accurately reproduces the deconfinement phase transition thermodynamics (pressure, energy density, trace anomaly) and predicts higher glueball excitations ($0^{++**}$, $0^{++***}$) consistent with available lattice calculations. This work establishes a new paradigm for data-driven holographic reconstruction, solving the long-standing challenge of unified description of confinement thermodynamics and spectroscopy. |
| title | Data-Driven Einstein-Dilaton Model for Pure Yang-Mills Thermodynamics and Glueball Spectrum |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2507.06729 |