Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
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arXiv
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2026
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| author | Li, Fu-Peng Pang, Long-Gang Qin, Guang-You |
| author_facet | Li, Fu-Peng Pang, Long-Gang Qin, Guang-You |
| contents | The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simulations of relativistic heavy-ion collisions. We construct a four-dimensional EoS using a deep-learning-assisted quasi-particle model (DLQPM) within a physics-informed neural network (PINN) framework, in which the masses of light quarks, strange quarks, and gluons are parameterized as functions of temperature and chemical potentials ($T, μ_B, μ_Q, μ_S$). The model is constrained by lattice QCD data at vanishing chemical potentials and provides a thermodynamically consistent extrapolation to finite $μ_{B,Q,S}$. The DLQPM accurately reproduces the lattice-calculated cumulants $χ^{B,Q,S}_{i,j,k}$ at $μ_{B,Q,S}=0$, and its predicted EoS at various chemical potentials agrees well with results from the generalized $T'$-expansion method in lattice QCD. Furthermore, the calculated baryon-strangeness correlation $C_{BS}$ is consistent, within uncertainties, with preliminary STAR data. This work offers a reliable EoS for exploring the QCD phase structure in the beam energy scan region. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_22352 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks Li, Fu-Peng Pang, Long-Gang Qin, Guang-You Nuclear Theory High Energy Physics - Phenomenology The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simulations of relativistic heavy-ion collisions. We construct a four-dimensional EoS using a deep-learning-assisted quasi-particle model (DLQPM) within a physics-informed neural network (PINN) framework, in which the masses of light quarks, strange quarks, and gluons are parameterized as functions of temperature and chemical potentials ($T, μ_B, μ_Q, μ_S$). The model is constrained by lattice QCD data at vanishing chemical potentials and provides a thermodynamically consistent extrapolation to finite $μ_{B,Q,S}$. The DLQPM accurately reproduces the lattice-calculated cumulants $χ^{B,Q,S}_{i,j,k}$ at $μ_{B,Q,S}=0$, and its predicted EoS at various chemical potentials agrees well with results from the generalized $T'$-expansion method in lattice QCD. Furthermore, the calculated baryon-strangeness correlation $C_{BS}$ is consistent, within uncertainties, with preliminary STAR data. This work offers a reliable EoS for exploring the QCD phase structure in the beam energy scan region. |
| title | Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks |
| topic | Nuclear Theory High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2604.22352 |