Application of the holographic equations of state for modeling experiments on heavy ion collisions

TL;DR

This work tackles constructing a holographic $EoS$ for quark-gluon plasma across the QCD phase diagram and embedding it into relativistic hydrodynamics for heavy-ion collision simulations. It adopts a bottom-up soft-wall AdS/QCD model with two dilaton fields and an anisotropic metric, calibrating key parameters against lattice QCD data using a two-stage machine-learning pipeline that includes regression surrogates and gradient-based optimization of $(a,b,d,G)$. The holographic $EoS$ is implemented in iEBE-MUSIC and vHLLE-SMASH frameworks to generate freeze-out hadron spectra, with $m_T$-spectra for $K^{+}$ demonstrating improved lattice agreement when using an alternative deforming factor. While the results show promise and comparability to lattice inputs, the authors acknowledge the need for further hydrodynamic parameter tuning and expanded observables to enable direct experimental comparisons. The study advances holographic modeling of QGP and informs future refinements in EoS calibration and multi-framework simulations.

Abstract

In this paper, we propose a method for numerical modeling of the nuclear matter properties within the framework of relativistic heavy-ion collisions using a holographic equation of state. Machine learning methods were applied to address the regression and optimization issues during the calibration of the relevant parameters using the LQCD results for quark masses that approximate the physical values. Numerical simulations are performed using the iEBE-MUSIC and vHLLE-SMASH frameworks, which incorporate certain relativistic hydrodynamics solvers. We modify the code by implementing a tabulated holographic equation of state, enabling simulations of quark-gluon plasma evolution with dynamically generated initial conditions via the 3D Monte Carlo Glauber Model and SMASH. Finally, the spectra of produced hadrons are computed using a hybrid iSS+UrQMD and Hadron Sampler+SMASH approaches at the freeze-out stage.12 p

Figures (8)

• Figure 1: Result of fitting $\frac{s}{T^3}$ ratio with the data from Ref. [12] for isotropic (on the left) and anisotropic (on the right) models with standard deforming factor
• Figure 2: The algortihm of solving the first task of calibration
• Figure 3: The algortihm of solving the second task of calibration
• Figure 4: Result of fitting ${s}/{T^3}$ ratio with the data from Ref. [12] for isotropic (on the left) and anisotropic (on the right) models with alternative deforming factor
• Figure 5: Results of $m_T$ spectra for $K^{+}$ calculations within the iEBE-MUSIC package for the standard deforming factor