Related papers: A Gaussian Process Generative Model for QCD Equati…
We employ the lattice QCD data on Taylor expansion coefficients up to sixth order to construct an equation of state at finite net-baryon density. When we take into account how hadron masses depend on lattice spacing and quark mass, the…
We study the QCD equation of state, and fluctuations of baryon number and strangeness using the hadron resonance gas model with repulsive mean field. We find that including both the predicted but not observed resonances, a.k.a. missing…
Bayesian methods are used to constrain the density dependence of the QCD Equation of State (EoS) for dense nuclear matter using the data of mean transverse kinetic energy and elliptic flow of protons from heavy ion collisions (HIC), in the…
We construct the QCD equation of state at finite chemical potentials including net baryon, electric charge, and strangeness based on the results of lattice QCD simulations and the hadron resonance gas model. The situation of strangeness…
Mean-field model quantum field theories of hadrons were traditionally developed to describe cold and dense nuclear matter and are by now very well constrained from the recent neutron star merger observations. We show that when augmented…
A phenomenological QCD quasiparticle model provides a means to map lattice QCD results to regions relevant for a variety of heavy-ion collision experiments at larger baryon density. We report on effects of collectives modes and damping on…
By using gravity/gauge correspondence, we construct a holographic model, constrained to mimic the lattice QCD equation of state at zero density, to investigate the temperature and baryon chemical potential dependence of the equation of…
In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger…
In this paper, we discuss the matching of the holographic equation of state with the equation of Hadron Resonance Gas for studying the nuclear matter properties within the framework of relativistic heavy-ion collisions. Machine learning…
Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum…
An attractive, energy-dependent mean-field potential for baryons is introduced in order to generate a soft region in the nuclear equation-of-state, as suggested by recent lattice QCD calculations of baryon free matter at finite temperature.…
We present results on the QCD equation of state, obtained with two different improved dynamical staggered fermion actions and almost physical quark masses. Lattice cut-off effects are discussed in detail as results for three different…
We construct the QCD equation of state at finite chemical potentials including net baryon, electric charge, and strangeness, based on the conserved charge susceptibilities determined from lattice QCD simulations and the equation of state of…
This article considers the generative modeling of the (mixed) states of quantum systems, and an approach based on denoising diffusion model is proposed. The key contribution is an algorithmic innovation that respects the physical nature of…
The properties of dense QCD matter are delineated through the construction of equations of state which should be consistent with the low and high density limits of QCD, nuclear laboratory experiments, and the neutron star observations.…
Various thermodynamic quantities for baryon-free matter are calculated by combining the most reliable non-perturbative and perturbative calculations, especially the most recent ones including as many quark flavors as possible. We extend…
Within Bayesian state estimation, considerable effort has been devoted to incorporating constraints into state estimation for process optimization, state monitoring, fault detection and control. Nonetheless, in the domain of state-space…
Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state…
The equation of state of QCD matter for temperatures near and above the quark-hadron transition (165 MeV) is inferred within a Bayesian framework through the comparison of data from the Relativistic Heavy Ion Collider and from the Large…
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…