相关论文: Extraction of the microscopic properties of quasi-…
The characteristics of a thermal system depend strongly on its response to thermal gradients and the underlying microscopic interactions among constituents. In the present study, we investigate the thermodynamic and transport properties of…
We extend the effective dynamical quasiparticle model (DQPM) - constructed for the description of non-perturbative QCD phenomena of the strongly interacting quark-gluon plasma (QGP) - to large baryon chemical potentials, $\mu_B$, including…
Dynamical quasiparticle properties are determined from lattice QCD along the line of the Peshier model for the running strong coupling constant in case of three light flavors. By separating time-like and space-like quantities in the number…
The properties of quantum-chromo dynamics (QCD) nowadays are accessable by lattice QCD calculations at vanishing quark chemical potential $\mu_q$=0 but often lack a transparent physical interpretation. In this review we report about results…
The interactions of quarks and gluons are strong at non-perturbative region. The equation of state (EoS) of a strongly-interacting quantum chromodynamics (QCD) medium can only be studied using the first-principle lattice QCD calculations.…
Deep neural networks (DNNs) are powerful tools for compressing and distilling information. Their scale and complexity, often involving billions of inter-dependent parameters, render direct microscopic analysis difficult. Under such…
We study the heavy quark scattering on partons of the quark gluon plasma (QGP) being especially interested in the collisional (elastic) scattering processes of heavy quarks on quarks and gluons. We calculate the different cross sections for…
We present calculations for the shear viscosity of the hot and dense quark-gluon plasma (QGP) using the partonic scattering cross sections as a function of temperature $T$ and baryon chemical potential $\mu_B$ from the dynamical…
We study the kinetic and chemical equilibration in "infinite" parton matter within the parton-hadron-string dynamics off-shell transport approach, which is based on a dynamical quasiparticle model (DQPM) for partons matched to reproduce…
Within the aim of a dynamical study of on- and off-shell heavy quarks Q in the quark gluon plasma (QGP) - as produced in relativistic nucleus-nucleus collisions - we study the heavy quark collisional scattering on partons of the QGP. The…
It is fascinating to predict the mass and width of the ordinary and exotic mesons solely based on their quark content and quantum numbers. Such prediction goes beyond conventional methodologies traditionally employed in hadron physics for…
We investigate the possibility of using quasi-normal modes (QNMs) to probe the microscopic structure of two-dimensional (2D) anti-de Sitter (AdS$_2$) dilatonic black holes. We first extend previous results on the QNMs spectrum, found for…
In this study, we analyze the transport properties of the Quark-Gluon Plasma, focusing on bulk ($\zeta$) and shear ($\eta$) viscosities at vanishing chemical potential. To describe the QGP, we employ a quasiparticle model for quarks along…
Generalized Parton Distribution functions (GPDs) are off-diagonal light-cone matrix elements that encode the internal structure of hadrons in terms of quark and gluon degrees of freedom. In this work, we present the first nonperturbative…
According to the dynamical quasiparticle model (DQPM) - matched to reproduce lattice QCD results in thermodynamic limit, - the constituents of the strongly interacting quark-gluon plasma (sQGP) are massive and off-shell quasi-particles…
We present a neural network-based quasi-particle model to separate the contributions of chromo-electric and chromo-magnetic gluons. Using dual residual networks, we extract temperature-dependent masses from SU(3) lattice thermodynamic data…
Redundancy in deep neural network (DNN) models has always been one of their most intriguing and important properties. DNNs have been shown to overparameterize, or extract a lot of redundant features. In this work, we explore the impact of…
We consider the ballistic transport of quasiparticles with exclusion statistics through a 1D wire within the Landauer-Buttiker approach. We demonstrate that quasiparticle transport coefficients (electrical and heat conductance, as well as…
Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for…
In this study we evaluate the dynamical collisional energy loss of heavy quarks, their interaction rate as well as the different transport coefficients (drag and diffusion coefficients, $\hat{q}$, etc). We calculate these different…