相关论文: HoloNet: Toward a Unified Einstein-Maxwell-Dilaton…
Within a neural-network-based holographic framework, we incorporate lattice QCD (LQCD) and Hadron Resonance Gas (HRG) data to train the model and predict the location of the QCD critical endpoint (CEP). The training dataset consists of the…
Based on lattice QCD results of equation of state (EOS) and baryon number susceptibility at zero baryon chemical potential, and supplemented by machine learning techniques, we construct the analytic form of the holographic black hole metric…
To quantitatively provide reliable predictions for the hot and dense QCD matter, a holographic model should be adjusted to describe first-principles lattice results available at vanishing baryon chemical potential. The equation of state…
We develop a thermodynamically consistent nonperturbative framework for equilibrium QCD criticality, unifying DSE quark propagation, FRG flow, and PNJL thermodynamics for coupled chiral/deconfinement order parameters. A holographic…
By using the AdS/CFT correspondence, we construct an Einstein-Maxwell-Dilaton model to map the thermodynamics of strongly interacting matter. The holographic model, constrained to reproduce the lattice QCD equation of state at zero baryon…
We present a fully back-reacted Einstein--Maxwell--Dilaton--flavor model with dynamical light and strange sectors, calibrated to lattice QCD using a machine-learning--assisted spectral method. The model reproduces the 2+1-flavor equation of…
We investigate several transport coefficients across the phase diagram of a holographic Einstein-Maxwell-Dilaton (EMD) model of hot and dense QCD with $N_f=2+1$ flavors. Our results are obtained from an open-source implementation of this…
We construct a holographic QCD model based on the Einstein--dilaton--flavor framework with 2+1 flavors and investigate its phase structure using machine-learning techniques. At zero chemical potential, the model reproduces the equation of…
In this review, we provide an up-to-date account of quantitative holographic descriptions of the strongly coupled quark-gluon plasma (QGP) produced in heavy-ion collisions, based on the class of gauge-gravity Einstein-Maxwell-Dilaton (EMD)…
We present a self-consistent dynamical holographic QCD model to investigate the mass spectra and melting behavior of heavy and exotic mesons at finite temperature and finite density. Our approach is based on the Einstein-Maxwell-Dilaton…
We investigate the QCD phase transition and its phase structure within Einstein-Maxwell-Dilaton-scalar system and compare the results with those obtained from the Einstein-Maxwell-Dilaton system. It is shown that both models reproduce…
Thermodynamics and transport are investigated in a holographic QCD model that extends the Einstein--Maxwell--Dilaton framework by incorporating Gauss--Bonnet corrections. Model parameters are fixed using state-of-the-art lattice QCD…
We consider the Einstein-Maxwell-dilaton system with an arbitrary kinetic gauge function and a dilaton potential. A family of analytic solutions is obtained by the potential reconstruction method. We then study its holographic dual QCD…
In this study, we investigate the behavior of entanglement properties in the QCD phase diagram using holographic Einstein-Maxwell-Dilaton (EMD) models. We consider two representative holographic QCD models and examine various entanglement…
We develop a data-driven neural network framework to reconstruct the five-dimensional background geometry, the dilaton potential, and the chiral-symmetry-breaking scalar potential of holographic QCD from hadron mass spectra. Framed as an…
Supplementing the holographic Einstein-Maxwell-dilaton model of [O. DeWolfe, S.S. Gubser, C. Rosen, Phys. Rev. D83 (2011) 086005; O. DeWolfe, S.S. Gubser, C. Rosen, Phys. Rev. D84 (2011) 126014] by input of lattice QCD data for 2+1 flavors…
By use of the potential reconstruction approach we obtain a series of asymptotically AdS (aAdS) black hole solutions in an Einstein-Maxwell-Dilaton (EMD) system. Basing on the solutions of the system, we reconstruct a semi-analytical…
Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine learning (QML)…
The entanglement exhibits extremal or singular behavior near quantum critical points (QCPs) in many condensed matter models. These intriguing phenomena, however, still call for a widely accepted understanding. In this letter we study this…
We consider the Einstein-Maxwell-dilaton system with an arbitrary kinetic gauge function and a dilaton potential. A family of analytic solutions is obtained by the potential reconstruction method. We then study its holographic dual QCD…