Introduction to QCD thermodynamics and the quark-gluon plasma

Gyulassy, Miklós

doi:10.1016/0146-6410(85)90076-6

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…(6.6). We may view the anti-screening as an effect of dispersion of the color charge [41]. For example, a static blue (B) quark may become red (R) by emitting a B R gluon.…”

Section: Discussionmentioning

confidence: 99%

Asymptotic Freedom from Thermal and Vacuum Magnetization

Persson

1996

Annals of Physics

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We calculate the effective Lagrangian for a magnetic field in spinor, scalar and vector QED. Connections are then made to $SU(N_C)$ Yang--Mills theory and QCD. The magnetization and the corresponding effective charge are obtained from the effective Lagrangian. The renormalized vacuum magnetization will depend on the renormalization scale chosen. Regardless of this, the effective charge decreasing with the magnetic field, as in QCD, corresponds to anti- screening and asymptotic freedom. In spinor and scalar QED on the other hand, the effective charge is increasing with the magnetic field, corresponding to screening. Including effects due to finite temperature and density, we comment on the effective charge in a degenerate fermion gas, increasing linearly with the chemical potential. Neglecting the tachyonic mode, we find that in hot QCD the effective charge is decreasing as the inverse temperature, in favor for the formation of a quark-gluon plasma. However, including the real part of the contribution from the tachyonic mode, we find instead an effective charge increasing with the temperature. Including a thermal gluon mass, the effective charge in hot QCD is group invariant (unlike in the two cases above), and decreases logarithmically in accordance to the vacuum renormalization group equation, with the temperature as the momentum scale.Comment: 38 pages. Latex. More sequential treatment of different limits. Thermal gluon mass include

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“…(6.6). We may view the anti-screening as an effect of dispersion of the color charge [41]. For example, a static blue (B) quark may become red (R) by emitting a B R gluon.…”

Section: Discussionmentioning

confidence: 99%

Asymptotic Freedom from Thermal and Vacuum Magnetization

Persson

1996

Annals of Physics

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“…Гібридна еволюційна нейромодель (GA -BPNN) була підготовлена для моделювання розподілу Rapidity distribution of hadron-nucleus (h-A) interactions have been studied as one example of the parameters controlling multiparticle production mechanism in high and ultrahigh energy physics. Many models studied the hadron structure [1][2][3][4][5] and the interactions between hadrons and nuclei such as fragmentation model [6][7][8], quark model [9][10][11], the three-fireball model [12] and many more.…”

Section: застосування генетичного алгоритму-алгоритму нейронної мереж...mentioning

confidence: 99%

The Application of the Genetic Algorithm-Back Propagation Neural Network Algorithm in the High-Energy Physics

El-Bakry,

El-Dahshan,

Radi

et al. 2016

East Eur. J. Phys.

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Multiparticle production mechanism is one of the most phenomena that the high-energy physics concerns. In this work, the evolutionary genetic algorithm (GA) is used to optimize the parameters of the back-propagation neural networks (BPNN). The hybrid evolutionary-neuro model (GA-BPNN) was trained to simulate the rapidity distribution 1/N(dN/dY) of positive and negative pions p-Au, p-Ag and p-Xe for p-Ar, p-Xe interactions at lab momentum Plab =100 GeV/c. Also, for total charged, positive and negative pions for interactions at Plab = 200 GeV/c. Finally, total charged particles for p- Pb collision at center-of-mass energy sqrt(s) = 5.02 TeV are simulated. An efficient ANN network with different connection parameters (weights and biases) have been designed by the GA to calculate and predict the rapidity distribution as a function of the lab momentum Plab, mass number (A) and the number of particles per unit solid angle (Y). Our simulated results have been compared with the experimental data and the matching has been clearly found. It is indicated that the developed GA-BPNN model for rapidity distribution was more successful.

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“…The history of studies of this interaction is therefore very long and extremely interesting from both the experimental and theoretical points of view. Different models are provided for the hadron structure [7][8][9][10], such as the three-fireball model [11], quark models [12][13][14], fragmentation model [15][16][17] and many others.…”

Section: Introductionmentioning

confidence: 99%

Application of genetic programming for proton-proton interactions

El‐Dahshan

2011

Open Physics

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Abstract:The aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P( )), the average multiplicity ( ) and the total cross section (σ ) at different values of high energies. We have obtained the multiplicity distribution as a function of the center of mass energy ( √ ) and charged particles ( ). Also, both the average multiplicity and the total cross section are obtained as a function of √ . Our discovered functions produced by GP technique show a good match to the experimental data. The performance of the GP models was also tested at non-trained data and was found to be in good agreement with the experimental data.PACS (

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Introduction to QCD thermodynamics and the quark-gluon plasma

Cited by 12 publications

References 38 publications

Asymptotic Freedom from Thermal and Vacuum Magnetization

Asymptotic Freedom from Thermal and Vacuum Magnetization

The Application of the Genetic Algorithm-Back Propagation Neural Network Algorithm in the High-Energy Physics

Application of genetic programming for proton-proton interactions

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