Explore the QCD phase transition phenomena from a multiphase transport model

Jin, Xipeng; Chen, Jinhui; Lin, Zi-Wei; Ma, Guo-Liang; Ma, Yugang; Zhang, Song

doi:10.1007/s11433-018-9272-4

Cited by 25 publications

(12 citation statements)

References 35 publications

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“…Hence, the string melting version is employed in this work. AMPT has given good descriptions of different physics for relativistic heavy-ion collisions at both RHIC [44] and LHC [51] energies, e.g., di-hadron azimuthal correlation [52,53], collective flow [54,55], strangeness production [56], chiral magnetic effect and so on [57,58].…”

Section: The Ampt Modelmentioning

confidence: 99%

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

He,

et al. 2021

Preprint

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α-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and three-α triangular (four-α tetrahedral) structure for 12 C ( 16 O), from heavyion collision events generated within a multi-phase transport (AMPT) model. Azimuthal angle and transverse momentum distributions of charged pions are taken as inputs to train the classifier. On multiple-event basis, the overall classification accuracy can reach 95% for 12 C/ 16 O + 197 Au events at √ SNN = 200 GeV. With proper constructions of samples, the predicted deviations on mixed samples with different proportions of both configurations could be within 5%. In addition, setting a simple confidence threshold can further improve the predictions on the mixed dataset. Our results indicate promising and extensive possibilities of application of machine-learning-based techniques to real data and some other problems in physics of heavy-ion collisions.

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Section: The Ampt Modelmentioning

confidence: 99%

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

He,

et al. 2021

Preprint

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“…The hadrons participate in rescattering process through a relativistic transport model [54]. AMPT was successful to describe physics in relativistic heavy-ion collisions for RHIC [50] and LHC [55], including pion-HBT correlations [56], di-hadron azimuthal correlations [57,58], collective flow [59,60] and strangeness production [61,62].…”

Section: Introductionmentioning

confidence: 99%

Collision system size scan of collective flows in relativistic heavy-ion collisions

Zhang

et al. 2020

Physics Letters B

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Initial geometrical distribution and fluctuation can affect the collective expansion in relativistic heavy-ion collisions. This effect may be more evident in small system (such as B + B) than in large one (Pb + Pb). This work presents the collision system dependence of collective flows and discusses about effects on collective flows from initial fluctuations in a framework of a multiphase transport model. The results shed light on system scan on experimental efforts to small system physics.

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“…AMPT simulates the relativistic heavy-ion collisions dynamically in a framework of multi phases, in which the initial state is given by the Heavy Ion Jet Interaction Generator (HIJING) model [35,36], and the melted partons from the HIJING interact with each other by the Zhang's Parton Cascade (ZPC) model [37], and finally the interacting-ceased partons converts to hadrons by a simple quark coalescence model or the Lund string fragmentation followed by a relativistic transport model performing the hadron rescattering [38]. AMPT can well describe physics in relativistic heavy-ion collisions at the RHIC [34] and LHC [39] energies, including pion-HBT correlations [40], di-hadron azimuthal correlations [41,42], collective flow [43,44] and strangeness production [45,46] and so on.…”

Section: A Brief Introduction To Algorithmmentioning

confidence: 99%

$Ω$-dibaryon production with hadron interaction potential from the lattice QCD in relativistic heavy-ion collisions

Zhang,

2020

Preprint

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Recently HAL QCD Collaboration reported the Ω − Ω and N − Ω interaction potentials by Lattice QCD algorithm. Based on these results, N Ω ( 5 S2) and ΩΩ ( 1 S0) bound state were proposed with binding energy about a few MeV and N − Ω HBT correlation were also calculated and measured by the STAR and the ALICE Collaboration. These results provided dynamical information if Ω-dibaryons exist from the interaction aspects. Another necessary point is the experimental environment where the bound states can produce and survive in the system or not, such as in relativistic heavy-ion collisions. So there are at least two necessary conditions to constrain the production probability of Ω-dibaryons, i.e. the short-range attractive interaction to form bound states and experimental environment to provide abundant enough strangeness and multiplicity of nucleons. In this work the Ω − Ω and Ω−nucleon interaction potentials by the lattice QCD were employed to obtain ΩΩ ( 1 S0) and N Ω ( 5 S2) wave functions, and the production of Ω-dibaryons was estimated by use of a dynamical coalescence mechanism with considering the environment in relativistic heavy-ion collisions at √ sNN = 200 GeV and 2.76 TeV.

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Explore the QCD phase transition phenomena from a multiphase transport model

Cited by 25 publications

References 35 publications

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

Collision system size scan of collective flows in relativistic heavy-ion collisions

$Ω$-dibaryon production with hadron interaction potential from the lattice QCD in relativistic heavy-ion collisions

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