Equation of State at High Baryon Densities from a Thermodynamically Informed Neural Network
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913152214499328 |
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| author | Adzhymambetov, Musfer |
| author_facet | Adzhymambetov, Musfer |
| contents | We present a four-dimensional equation of state for strongly interacting matter at finite temperature and conserved charge densities, constructed using a deep neural network. It is designed for direct use in hybrid models of relativistic heavy-ion collisions: it reproduces hadron resonance gas thermodynamics at typical particlization scales, is consistent with lattice QCD at low baryon chemical potential, and extrapolates into the high-density region inaccessible to either approach, which is precisely the regime targeted by RHIC BES, FAIR, HADES, and CBM. Thermodynamic consistency throughout the full phase space is enforced via a physics-informed loss function. We demonstrate the developed equation of state by implementing it at zero net strangeness and fixed electric-to-baryon charge ratio within the integrated hydrokinetic model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22199 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Equation of State at High Baryon Densities from a Thermodynamically Informed Neural Network Adzhymambetov, Musfer High Energy Physics - Phenomenology Nuclear Theory We present a four-dimensional equation of state for strongly interacting matter at finite temperature and conserved charge densities, constructed using a deep neural network. It is designed for direct use in hybrid models of relativistic heavy-ion collisions: it reproduces hadron resonance gas thermodynamics at typical particlization scales, is consistent with lattice QCD at low baryon chemical potential, and extrapolates into the high-density region inaccessible to either approach, which is precisely the regime targeted by RHIC BES, FAIR, HADES, and CBM. Thermodynamic consistency throughout the full phase space is enforced via a physics-informed loss function. We demonstrate the developed equation of state by implementing it at zero net strangeness and fixed electric-to-baryon charge ratio within the integrated hydrokinetic model. |
| title | Equation of State at High Baryon Densities from a Thermodynamically Informed Neural Network |
| topic | High Energy Physics - Phenomenology Nuclear Theory |
| url | https://arxiv.org/abs/2605.22199 |