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Nuclear Theory·nucl-th

Thursday·January 11, 2018

4 papers—1 primary·3 cross-listed

All4Primary1Cross-lists3
  1. 01

    Applications of deep learning to relativistic hydrodynamics

    Hengfeng Huang🇨🇳 · Bowen Xiao🇨🇳 · Ziming Liu🇨🇳 · Zeming Wu🇨🇳 · Yadong Mu🇨🇳 · Huichao Song🇨🇳

    Relativistic hydrodynamics is a powerful tool to simulate the evolution of the quark gluon plasma (QGP) in relativistic heavy ion collisions. Using 10000 initial and final profiles generated from 2+1-d relativistic hydrodynamics VISH2+1 with MC-Glauber initial conditions, we train a deep neural network based on stacked U-net, and use it to predict the final profiles associated with various initial conditions, including MC-Glauber, MC-KLN and AMPT and TRENTo. A comparison with the VISH2+1 results shows that the network predictions can nicely capture the magnitude and inhomogeneous structures of the final profiles, and nicely describe the related eccentricity distributions (n=2, 3, 4). These results indicate that deep learning technique can capture the main features of the non-linear evolution of hydrodynamics, showing its potential to largely accelerate the event-by-event simulations of relativistic hydrodynamics.

    nucl-thastro-ph.HEcond-mat.dis-nnhep-phPRResearch(2021)·32 citations
  2. 02

    QCD thermodynamics from lattice calculations with non-equilibrium methods: The SU(3) equation of state

    Michele Caselle🇮🇹 · Alessandro Nada🇮🇹 · Marco Panero🇮🇹

    A precise lattice determination of the equation of state in SU(3) Yang-Mills theory is carried out by means of a simulation algorithm, based on Jarzynski's theorem, that allows one to compute physical quantities in thermodynamic equilibrium, by driving the field configurations of the system out of equilibrium. The physical results and the computational efficiency of the algorithm are compared with other state-of-the-art lattice calculations, and the extension to full QCD with dynamical fermions and to other observables is discussed.

    ↳ hep-lathep-phhep-thnucl-thPRD(2018)·62 citations
  3. 03

    Multifractal Characteristics of Multiparticle Production in Heavy-Ion Collisions at SPS Energies

    Shaista Khan🇮🇳 · Shakeel Ahmad🇮🇳

    Entropy, dimensions and other multifractal characteristics of multiplicity distributions of relativistic charged hadrons produced in ion-ion collisions at SPS energies are investigated. The analysis of the experimental data is carried out in terms of phase space bin-size dependence of multiplicity distributions following the Takagi's approach. Yet another method is also followed to study the multifractality which, is not related to the bin-width and (or) the detector resolution, rather involves multiplicity distribution of charged particles in full phase space in terms of information entropy and its generalization, Rényi's order-q information entropy. The findings reveal the presence of multifractal structure-- a remarkable property of the fluctuations. Nearly constant values of multifractal specific heat, 'c' estimated by the two different methods of analysis followed indicate that the parameter 'c' may be used as a universal characteristic of the particle production in high energy collisions. The results obtained from the analysis of the experimental data agree well with the predictions of Monte Carlo model AMPT.

    ↳ hep-phnucl-thIJMPE(2018)·8 citations
  4. 04

    Small System Collectivity in Relativistic Hadron and Nuclear Collisions

    J.L. Nagle🇺🇸 · W.A. Zajc🇺🇸

    The bulk motion of nuclear matter at the ultra-high temperatures created in heavy-ion collisions at the Relativistic Heavy Ion Collider and the Large Hadron Collider is well described in terms of nearly inviscid hydrodynamics, thereby establishing this system of quarks and gluons as the most perfect fluid in nature. A revolution in the field is underway, spearheaded by the discovery of similar collective, fluid-like phenomena in much smaller systems including p+p, p+A, d+Au, and HeAu collisions. We review these exciting new observations and their implications.

    ↳ nucl-exhep-phnucl-thAnn.Rev.Nucl.Part.Sci.(2018)·471 citations

Affiliations

first authorsco-authorsvia INSPIRE