Nuclear equation of state at finite $μ_B$ using deep learning assisted quasi-parton model
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866929680017260544 |
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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 | To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential ($μ_B$) remains a challenging yet essential goal in the study of QCD matter under extreme conditions. In this study, we develop a deep learning assisted quasi-parton model, which utilizes three deep neural networks, to reconstruct the QCD EoS at zero $μ_B$ and predict the EoS and transport coefficient $η/s$ at finite $μ_B$. The EoS derived from our quasi-parton model shows excellent agreement with lattice QCD results obtained using Taylor expansion techniques. The minimum value of $η/s$ is found to be approximately 175 MeV and decreases with increasing chemical potential within the confidence interval. This model not only provides a robust framework for understanding the properties of the QCD EoS at finite $μ_B$ but also offers critical input for relativistic hydrodynamic simulations of nuclear matter produced in heavy-ion collisions by the RHIC beam energy scan program. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nuclear equation of state at finite $μ_B$ using deep learning assisted quasi-parton model Li, Fu-Peng Pang, Long-Gang Qin, Guang-You Nuclear Theory High Energy Physics - Phenomenology To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential ($μ_B$) remains a challenging yet essential goal in the study of QCD matter under extreme conditions. In this study, we develop a deep learning assisted quasi-parton model, which utilizes three deep neural networks, to reconstruct the QCD EoS at zero $μ_B$ and predict the EoS and transport coefficient $η/s$ at finite $μ_B$. The EoS derived from our quasi-parton model shows excellent agreement with lattice QCD results obtained using Taylor expansion techniques. The minimum value of $η/s$ is found to be approximately 175 MeV and decreases with increasing chemical potential within the confidence interval. This model not only provides a robust framework for understanding the properties of the QCD EoS at finite $μ_B$ but also offers critical input for relativistic hydrodynamic simulations of nuclear matter produced in heavy-ion collisions by the RHIC beam energy scan program. |
| title | Nuclear equation of state at finite $μ_B$ using deep learning assisted quasi-parton model |
| topic | Nuclear Theory High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2501.10012 |