Phenomenological Review on Quark–Gluon Plasma: Concepts vs. Observations

Pasechnik, Roman; Šumbera, M.

doi:10.3390/universe3010007

Cited by 131 publications

(110 citation statements)

References 397 publications

(650 reference statements)

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“…It is the state of matter existing at extremely high temperatures in QCD. These conditions can be created in relativistic heavy-ion collisions [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Speed of sound in the QGP and an SU(3) Yang-Mills theory

2018

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The speed of sound Cs in the SU(3) and (2+1)QCD is calculated within the Field Correlator Method using the nonperturbative colour magnetic confinement and Polyakov loop interaction in the deconfined region.The resulting Cs displays a discontinuity at T = Tc in the SU(3) case. It is shown numerically and analytically that C 2 s never exceeds 1 3 both for SU (3) and (2+1) QCD for vanishing chemical potential.A good agreement is found of our numerical results with the corresponding lattice data.

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“…It is the state of matter existing at extremely high temperatures in QCD. These conditions can be created in relativistic heavy-ion collisions [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Speed of sound in the QGP and an SU(3) Yang-Mills theory

2018

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“…Probes of the QGP phase had been found by large experimental efforts at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) [17,18,19,20].…”

Section: Introductionmentioning

confidence: 99%

String percolation threshold for elliptically bounded systems

Ramírez¹,

Téllez²,

Bautista³

2017

Physica A: Statistical Mechanics and its Applications

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It has been shown that a hot and dense deconfined nuclear matter state produced in ultra-relativistic heavy-ion collisions, can be quantitatively described by the String Percolation phenomenological model. The model address the phase transition in terms of the two-dimensional continuum percolation theory over strings, which are schematic representations of the fundamental interactions among the partons of the colliding nuclei in the initial state. In this work, we present an extension of the critical string density results including the eccentricity dependence on the initial state geometry focus on small string number with different density profile, small deviations from the different profile densities are found. The percolation threshold shows consistency with the thermodynamic limit for the uniform density profile with a large number of strings in the case of circular boundary system. A significant dependence on the eccentricity for a small number of strings compared to high occupancy systems is exhibited, the implications may become relevant in hadron-hadron or hadron-nucleus collision systems.

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“…Studies of phase transition are connected to various areas among theoretical/experimental physics. [1][2][3][4][5][6][7] Calculating order parameters is one of the conventional ways to define phases and phase transitions. However, some phases like topological phases 8) do not have any clear order parameters.…”

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confidence: 99%

Detection of Phase Transition via Convolutional Neural Networks

2017

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A Convolutional Neural Network (CNN) is designed to study correlation between the temperature and the spin configuration of the 2 dimensional Ising model. Our CNN is able to find the characteristic feature of the phase transition without prior knowledge. Also a novel order parameter on the basis of the CNN is introduced to identify the location of the critical temperature; the result is found to be consistent with the exact value.Studies of phase transition are connected to various areas among theoretical/experimental physics. 1-7) Calculating order parameters is one of the conventional ways to define phases and phase transitions. However, some phases like topological phases 8) do not have any clear order parameters. Even if there are certain theoretical order parameters like entanglement entropy, 9,10) they are difficult to measure in experiments.Machine learning (ML) techniques are useful to resolve this undesirable situation. In fact, ML techniques have been already applied to various problems in theoretical physics: finding approximate potential surface, 11) a study of transition in glassy liquids, 12) solving mean-field equations 14) and quantum many-body systems, 13, 15) a study of topological phases. 16) Especially, ML techniques based on convolutional neural network (CNN) have been developing since the recent groundbreaking record 17) in ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012), 18) and it is applied to investigate phases of matters with great successes on classifications of phases in 2D systems [19][20][21] and 3D systems. 22,23) It is even possible to draw phase diagrams. 23) In these previous works, however, one needs some informations of the answers for the problems a priori. For example, to classify phases of a system, the training process requires the values of critical temperatures or the location of phase boundaries. This fact prevents applications of the ML techniques to unknown systems so far.The learning process without any answers is called unsupervised learning. Indeed, there are known results on detecting the phase transitions based on typical unsupervised learning architectures called autoencoder which is equivalent to principal component analysis 32) and its variant called variational autoencoder. ?) These architectures encode informations of given samples to lower dimensional vectors, and it is pointed out that such encoding process is similar to encoding physical state informations to order parameters of the systems. However, it is not evident whether the latent variables provide the critical temperature.We propose a novel but simple prescription to estimate the critical temperature of the system via neural network (NN) based on ML techniques without a priori knowledge of the order parameter. Throughout this letter, we focus on the fer- * akinori.tanaka@riken.jp † akio.tomiya@mail.ccnu.edu.cn m m Fig. 1. '(Color online)' Plots of weight matrix components in convolutional neural network (left) and fully connected neural network (right). Horizontal axis correspon...

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Phenomenological Review on Quark–Gluon Plasma: Concepts vs. Observations

Cited by 131 publications

References 397 publications

Speed of sound in the QGP and an SU(3) Yang-Mills theory

Speed of sound in the QGP and an SU(3) Yang-Mills theory

String percolation threshold for elliptically bounded systems

Detection of Phase Transition via Convolutional Neural Networks

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