相关论文: Neural network enhanced Bayesian global analysis o…

We extend the applicability of the hydrodynamics, perturbative QCD and saturation -based EKRT (Eskola-Kajantie-Ruuskanen-Tuominen) framework for ultrarelativistic heavy-ion collisions to peripheral collisions by introducing dynamical…

We train a deep convolutional neural network to predict hydrodynamic results for flow coefficients, average transverse momenta and charged particle multiplicities in ultrarelativistic heavy-ion collisions from the initial energy density…

We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present a data-driven deep learning framework for predicting…

We demonstrate how deep convolutional neural networks can be trained to predict 2+1 D hydrodynamic simulation results for flow coefficients, mean-transverse-momentum and charged particle multiplicity from the initial energy density profile.…

We compute the initial energy densities produced in ultrarelativistic heavy-ion collisions from NLO perturbative QCD using a saturation conjecture to control soft particle production, and describe the subsequent space-time evolution of the…

The transport properties of the strongly-coupled quark-gluon plasma created in ultra-relativistic heavy-ion collisions are extracted by Bayesian parameter estimate methods with the latest collision beam energy data from LHC. This Bayesian…

In this contribution we briefly give an overview of the theoretical models used to describe experimental data from heavy-ion collisions from $\sqrt{s_{NN}} \approx $ 4 GeV to ultra-relativistic energies of $\sqrt{s_{NN}} \approx $ 5 TeV. We…

Hybrid approaches based on relativistic hydrodynamics and transport theory have been successfully applied for many years for the dynamical description of heavy ion collisions at ultrarelativistic energies. In this work a new viscous hybrid…

We apply a 3+1D viscous hydrodynamic + cascade model to the heavy ion collision reactions with $\sqrt{s_{NN}}=6.3\dots39$ GeV. To accommodate the model for a given collision energy range, the initial conditions for hydrodynamic phase are…

Using a hybrid (viscous hydrodynamics + hadronic cascade) framework, we model the bulk dynamical evolution of relativistic heavy-ion collisions at Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan (BES) collision energies, including…

In this dissertation I introduce relativistic heavy ion collisions and describe theoretical approaches to understanding them--in particular, viscous hydrodynamic simulations and investigations of final state interactions. The successful…

Improved constraints on current model parameters in a heavy-ion collision model are established using the latest measurements from three distinct collision systems. Various observables are utilized from Au--Au collisions at…

We quantitatively estimate properties of the quark-gluon plasma created in ultra-relativistic heavy-ion collisions utilizing Bayesian statistics and a multi-parameter model-to-data comparison. The study is performed using a recently…

We perform a global Bayesian analysis of a modern event-by-event heavy-ion collision model and LHC data at $\sqrt s$ = 2.76 and 5.02 TeV. After calibration, the model simultaneously describes multiplicity, transverse momentum, and flow data…

A global Bayesian analysis of relativistic Pb + Pb collisions at $\sqrt{s}_{\rm NN}$ = 2.76 TeV is performed, using a multistage model consisting of an IP-Glasma initial state, a viscous fluid dynamical evolution, and a hadronic transport…

This work presents a Bayesian inference study for relativistic heavy-ion collisions in the Beam Energy Scan program at the Relativistic Heavy-Ion Collider. The theoretical model simulates event-by-event (3+1)D collision dynamics using…

A deep learning based method with Convolutional Neural Network (CNN) algorithm is developed for simultaneous determination of the Elliptic Flow coefficient ($v_{2}$) and the Impact Parameter in Heavy-Ion Collisions at relativistic energies.…

In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is…

A Bayesian calibration, using experimental data from 2.76 $A$ TeV Pb-Pb collisions at the LHC, of a novel hybrid model is presented in which the usual pre-hydrodynamic and viscous relativistic fluid dynamic (vRFD) stages are replaced by a…

In this work, a Bayesian statistical framework is employed to analyze particle yield ratios in Au-Au collisions, utilizing Non-Extensive Statistics (NES). Through Markov Chain Monte Carlo (MCMC) sampling, we systematically estimate key…