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Register a new userIdentifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
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Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade after-burner. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.
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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 coupled with a hadronic cascade after-burner. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.
We argue that by measuring higher moments of the net proton number fluctuations in heavy ion collisions (HIC) one can probe the QCD chiral cross over transition experimentally. We discuss the properties of fluctuations of the net baryon number in the vicinity of the chiral crossover transition within the Polyakov loop extended quark-meson model at finite temperature and baryon density. The calculation includes non-perturbative dynamics implemented within the functional renormalization group approach. We find a clear signal for the chiral crossover transition in the fluctuations of the net baryon number. We address our theoretical findings to experimental data of STAR Collaboration on energy and centrality dependence of the net proton number fluctuations and their probability distributions in HIC.
Working in the linear sigma model with quarks, we compute the finite-temperature effective potential in the presence of a weak magnetic field, including the contribution of the pion ring diagrams and considering the sigma as a classical field. In the approximation where the pion self-energy is computed perturbatively, we show that there is a region of the parameter space where the effect of the ring diagrams is to preclude the phase transition from happening. Inclusion of the magnetic field has small effects that however become more important as the system evolves to the lowest temperatures allowed in the analysis.
The final stage of a relativistic heavy-ion collision is a hadron gas. Final-state interactions therein distort the $p_T$ spectrum of particles coming from the phase transition upon cooling the quark-gluon plasma. Using recent state-of-the-art parametrizations of pion interactions we provide theoretical computations of the pionic depth of the gas: how likely is it that a given pion rescatters in it (we find a high probability around $p_T=0.5$ GeV at midrapidity, corresponding to the formation of the $rho$ resonance), a comparison of the collision and Bjorken expansion rates, and how many pions make it through without interacting as a function of $p_T$. This is in the range 10-24$%$ and shown in this plot, the main result of the contribution.
We study the diffusion of charm quarks in the early stage of high energy nuclear collisions at the RHIC and the LHC. The main novelty of the present study is the introduction of the color current carried by the heavy quarks that propagate in the evolving Glasma (Ev-Glasma), that is responsible of the energy loss via polarization of the medium. We compute the transverse momentum broadening, $sigma_p$, of charm in the pre-thermalization stage, and the impact of the diffusion on the nuclear modification factor in nucleus-nucleus collisions. The net effect of energy loss is marginal in the pre-thermalization stage. The study is completed by the calculation of coordinate spreading, $sigma_x$, and by a comparison with Langevin dynamics. $sigma_p$ in Ev-Glasma overshoots the result of standard Langevin dynamics at the end of the pre-hydro regime. We interpret this as a result of memory of the color force acting on the charm quarks that implies $sigma_ppropto t^2$. Moreover, $sigma_xpropto t^2 $ in the pre-hydro stage shows that the charm quark in the Ev-Glasma is in the regime of ballistic diffusion.
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