Mini-jet thermalization and diffusion of transverse momentum correlation in high-energy heavy-ion collisions

Transverse momentum correlation in azimuthal angle of produced hadrons due to mini-jets are studied first within the HIJING Monte Carlo model in high-energy heavy-ion collisions. Jet quenching in the early stage of thermalization is shown to lead to significant diffusion (broadening) of the correlation. Evolution of the transverse momentum density fluctuation that gives rise to such correlation in azimuthal angle in the later stage of heavy-ion collisions is further investigated within a linearized diffusion-like equation and is shown to be determined by the shear viscosity of the evolving dense matter. Such a diffusion equation for the transverse momentum fluctuation is solved with initial values given by HIJING and together with the hydrodynamic equation for the bulk medium. The final transverse momentum correlation in azimuthal angle is calculated along the freeze-out hyper-surface and is found further diffused for larger values of shear viscosity to entropy density ratio $\eta/s \sim 0.2-0.4$. Therefore the final transverse momentum correlation in azimuthal angle can be used to study the thermalization of mini-jets in the early stage of heavy-ion collisions and the viscous effect in the hydrodynamic evolution of the strongly coupled quark gluon plasma.Comment: RevTex 4, 4 pages and 2 figures, the method to determine the fluctuation in transverse fluid velocity in the initial time of the hydro evolution has been improved. The relevant parts have been rewritten with some discussions and references adde

Vortical fluid and Λ\Lambda spin correlations in high-energy heavy-ion collisions

Fermions become polarized in a vortical fluid due to spin-vorticity coupling. The spin polarization density is proportional to the local fluid vorticity at the next-to-leading order of a gradient expansion in a quantum kinetic theory. Spin correlations of two $\Lambda$-hyperons can therefore reveal the vortical structure of the dense matter in high-energy heavy-ion collisions. We employ a (3+1)D viscous hydrodynamic model with event-by-event fluctuating initial conditions from A MultiPhase Transport (AMPT) model to calculate the vorticity distributions and $\Lambda$ spin correlations. The azimuthal correlation of the transverse spin is shown to have a cosine form plus an offset due to a circular structure of the transverse vorticity around the beam direction and global spin polarization. The longitudinal spin correlation shows a structure of vortex-pairing in the transverse plane due to the convective flow of hot spots in the radial direction. The dependence on colliding energy, rapidity, centrality and sensitivity to the shear viscosity are also investigated.Comment: 5 pages in Latex, 3 figure

Effects of jet-induced medium excitation in γ\gamma-hadron correlation in A+A collisions

Coupled Linear Boltzmann Transport and hydrodynamics (CoLBT-hydro) is developed for co-current and event-by-event simulations of jet transport and jet-induced medium excitation (j.i.m.e.) in high-energy heavy-ion collisions. This is made possible by a GPU parallelized (3+1)D hydrodynamics that has a source term from the energy-momentum deposition by propagating jet shower partons and provides real time update of the bulk medium evolution for subsequent jet transport. Hadron spectra in $\gamma$-jet events of A+A collisions at RHIC and LHC are calculated for the first time that include hadrons from both the modified jet and j.i.m.e.. CoLBT-hydro describes well experimental data at RHIC on the suppression of leading hadrons due to parton energy loss. It also predicts the enhancement of soft hadrons from j.i.m.e. The onset of soft hadron enhancement occurs at a constant transverse momentum due to the thermal nature of soft hadrons from j.i.m.e. which also have a significantly broadened azimuthal distribution relative to the jet direction. Soft hadrons in the $\gamma$ direction are, on the other hand, depleted due to a diffusion wake behind the jet.Comment: 4 pages, 4 figures in LaTeX, final version published in PL

Bayesian extraction of jet energy loss distributions in heavy-ion collisions

Based on the factorization in perturbative QCD, a jet cross sections in heavy-ion collisions can be expressed as a convolution of the jet cross section in $p+p$ collisions and a jet energy loss distribution. Using this simple expression and the Markov Chain Monte Carlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and $\gamma$-triggered jets in $Pb+Pb$ collisions with different centralities at two colliding energies at the Large Hadron Collider. The average jet energy loss has a dependence on the initial jet energy that is slightly stronger than a logarithmic form and decreases from central to peripheral collisions. The extracted jet energy loss distributions with a scaling behavior in $x=\Delta p_T /\langle \Delta p_T\rangle$ have a large width. These are consistent with the linear Boltzmann transport model simulations, in which the observed jet quenching is caused on the average by only a few out-of-cone scatterings.Comment: 5 pages in RevTex, 3 figures, final version to appear in Phys. Rev. Letter

Gradient Tomography of Jet Quenching in Heavy-Ion Collisions

Transverse momentum broadening and energy loss of a propagating parton are dictated by the space-time profile of the jet transport coefficient $\hat q$ in a dense QCD medium. The spatial gradient of $\hat q$ perpendicular to the propagation direction can lead to a drift and asymmetry in parton transverse momentum distribution. Such an asymmetry depends on both the spatial position along the transverse gradient and path length of a propagating parton as shown by numerical solutions of the Boltzmann transport in the simplified form of a drift-diffusion equation. In high-energy heavy-ion collisions, this asymmetry with respect to a plane defined by the beam and trigger particle (photon, hadron or jet) with a given orientation relative to the event plane is shown to be closely related to the transverse position of the initial jet production in full event-by-event simulations within the linear Boltzmann transport model. Such a gradient tomography can be used to localize the initial jet production position for more detailed study of jet quenching and properties of the quark-gluon plasma along a given propagation path in heavy-ion collisions.Comment: 5 pages in RevTex with 4 figures, final published version in PR

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure