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Register a new userClassification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
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Eugene Zabrodin
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle distributions of protons are used to train a classifier. An overall accuracy of classification of 98% is reached for Au+Au events at $sqrt{s_{NN}} = 11$ GeV. Performance of classifiers, trained on events at different colliding energies, is investigated. Obtained results indicate extensive possibilities of application of Deep Learning methods to other problems in physics of heavy-ion collisions.
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Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the simulated data of heavy-ion collisions, hiding weak signals of a few inter-particle correlations within a large particle cloud. Employing a point cloud network with dynamical edge convolution, we are able to identify events with critical fluctuations through supervised learning, and pick out a large fraction of signal particles used for decision-making in each single event.
We study the formation of large hyper-fragments in relativistic heavy-ion collisions within two transport models, DCM and UrQMD. Our goal is to explore a new mechanism for the formation of strange nuclear systems via capture of hyperons by relatively cold spectator matter produced in semi-peripheral collisions. We investigate basic characteristics of the produced hyper-spectators and evaluate the production probabilities of multi-strange systems. Advantages of the proposed mechanisms over an alternative coalescence mechanism are analysed. We also discuss how such systems can be detected taking into account the background of free hyperons. This investigation is important for the development of new experimental methods for producing hyper-nuclei in peripheral relativistic nucleus-nucleus collisions, which are now underway at GSI and are planned for the future FAIR and NICA facilities.
We study charm production in ultra-relativistic heavy-ion collisions by using the Parton-Hadron-String Dynamics (PHSD) transport approach. The initial charm quarks are produced by the PYTHIA event generator tuned to fit the transverse momentum spectrum and rapidity distribution of charm quarks from Fixed-Order Next-to-Leading Logarithm (FONLL) calculations. The produced charm quarks scatter in the quark-gluon plasma (QGP) with the off-shell partons whose masses and widths are given by the Dynamical Quasi-Particle Model (DQPM), which reproduces the lattice QCD equation-of-state in thermal equilibrium. The relevant cross sections are calculated in a consistent way by employing the effective propagators and couplings from the DQPM. Close to the critical energy density of the phase transition, the charm quarks are hadronized into $D$ mesons through coalescence and/or fragmentation. The hadronized $D$ mesons then interact with the various hadrons in the hadronic phase with cross sections calculated in an effective lagrangian approach with heavy-quark spin symmetry. The nuclear modification factor $R_{AA}$ and the elliptic flow $v_2$ of $D^0$ mesons from PHSD are compared with the experimental data from the STAR Collaboration for Au+Au collisions at $sqrt{s_{NN}}$ =200 GeV and to the ALICE data for Pb+Pb collisions at $sqrt{s_{NN}}$ =2.76 TeV. We find that in the PHSD the energy loss of $D$ mesons at high $p_T$ can be dominantly attributed to partonic scattering while the actual shape of $R_{AA}$ versus $p_T$ reflects the heavy-quark hadronization scenario, i.e. coalescence versus fragmentation. Also the hadronic rescattering is important for the $R_{AA}$ at low $p_T$ and enhances the $D$-meson elliptic flow $v_2$.
Heavy flavor probes are sensitive to the properties of the quark gluon plasma (QGP) produced in relativistic heavy-ion collisions. A huge amount of effort has been devoted to studying different aspects of the heavy-ion collisions using heavy flavor particles. In this work, we study the dynamics of heavy quark transport in the QGP medium using the rapidity dependence of heavy flavor observables. We calculate the nuclear modification of $text{B}$ and $text{D}$ meson spectra as well as spectra of leptons from heavy flavor decays in the rapidity range $[-4.0,4.0]$. We use an implementation of the improved Langevin equation with gluon radiation on top of a (3+1)-dimensional relativistic viscous hydrodynamical background for several collision setups. We find that the rapidity dependence of the heavy quark modification is determined by the interplay between the smaller size of the medium, which affects the path length of the heavy quarks, and the softer heavy quark initial production spectrum. We compare our results with available experimental data and present predictions for open heavy flavor meson $R_text{AA}$ at finite rapidity.
In this paper, we implement Principal Component Analysis (PCA) to study the single particle distributions generated from thousands of {tt VISH2+1} hydrodynamic simulations with an aim to explore if a machine could directly discover flow from the huge amount of data without explicit instructions from human-beings. We found that the obtained PCA eigenvectors are similar to but not identical with the traditional Fourier bases. Correspondingly, the PCA defined flow harmonics $v_n^prime$ are also similar to the traditional $v_n$ for $n=2$ and 3, but largely deviated from the Fourier ones for $ngeq 4$. A further study on the symmetric cumulants and the Pearson coefficients indicates that mode-coupling effects are reduced for these flow harmonics defined by PCA.
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