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Abstract <p> We present and implement two approaches for including the strangeness potential in the holographic equation of state together with the baryon chemical potential. The first approach is based on the concept of a free quark gas, taking into account symmetry considerations, while the second is based on the thermodynamic properties of the hadron gas. The goal of this paper is to study the effect of strangeness on the evolution of quark–gluon plasma within the framework of hydrodynamic modeling of heavy-ion collisions. The parameters of the holographic model are calibrated to lattice quantum chromodynamics data with the physical masses of quarks using machine learning methods. The equation of state is integrated into the MUSIC and vHLLE packages, and multistage modeling of ion collisions is performed using the iEBE-MUSIC and SMASH-vHLLE frameworks. It is shown that the inclusion of strangeness based on the hadron gas leads to better agreement with the experimental spectra of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(K^+\)</EquationSource> </InlineEquation> mesons compared to the hypothesis of a quark gas, and the sensitivity to the choice of the hypothesis strongly depends on the hydrodynamic package used. </p>

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Holographic equation of state with strangeness for the study of the quark–gluon plasma evolution

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • A. V. Anufriev,
  • V. N. Kovalenko

摘要

Abstract

We present and implement two approaches for including the strangeness potential in the holographic equation of state together with the baryon chemical potential. The first approach is based on the concept of a free quark gas, taking into account symmetry considerations, while the second is based on the thermodynamic properties of the hadron gas. The goal of this paper is to study the effect of strangeness on the evolution of quark–gluon plasma within the framework of hydrodynamic modeling of heavy-ion collisions. The parameters of the holographic model are calibrated to lattice quantum chromodynamics data with the physical masses of quarks using machine learning methods. The equation of state is integrated into the MUSIC and vHLLE packages, and multistage modeling of ion collisions is performed using the iEBE-MUSIC and SMASH-vHLLE frameworks. It is shown that the inclusion of strangeness based on the hadron gas leads to better agreement with the experimental spectra of \(K^+\) mesons compared to the hypothesis of a quark gas, and the sensitivity to the choice of the hypothesis strongly depends on the hydrodynamic package used.