The Parameters of The Tsallis Distribution at the LHC

Cleymans, J.; Azmi, M. D.; Parvan, A.S.; Teryaev, Oleg

doi:10.1051/epjconf/201713711004

Cited by 16 publications

(12 citation statements)

References 28 publications

(25 reference statements)

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“…With (32) and the parametrizations of the Tsallis -Pareto parameters above, (29)- (31) we can calculate the yield of a given hadron species at any center-of-mass energy and centrality, using the corresponding number of participant nucleons, N part , taken from experimental data. The integrated yields per unit rapidity for various identified hadrons stemming from √ s N N = 2.76 TeV PbPb collisions with different centrality classes measured by ALICE are shown on figure 5a with red circles, the most central data located on the top [57,58,72,74,75].…”

Section: Multiplicity Scalingmentioning

confidence: 99%

Tsallis-thermometer: a QGP indicator for large and small collisional systems

Bíró¹,

Barnaföldi²,

Bı́ró³

2020

J. Phys. G: Nucl. Part. Phys.

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The transverse momentum distribution of identified hadrons from recent years are analyzed within the thermodynamically consistent formulation of nonextensive statistics. A wide range of center-of-mass energies and average event multiplicities are studied for various hadron species. We demonstrate that the average event multiplicity is a key variable in the study of high-energy collisions and a smooth transition in the description from small to large systems is possible. For this purpose the non-extensive statistical approach is more than appropriate. The validity of the non-extensive description is explored in multiple ways: such as the calculation of integrated yields per unit rapidity, the analysis of radial flow and the consistency check of the thermodynamical variables. The 'Tsallis-thermometer' is introduced as an indicator of quark-gluon plasma in small collisional systems.

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Section: Multiplicity Scalingmentioning

confidence: 99%

Tsallis-thermometer: a QGP indicator for large and small collisional systems

Bíró¹,

Barnaföldi²,

Bı́ró³

2020

J. Phys. G: Nucl. Part. Phys.

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“…The value of q − 1 > 0 is small in many cases [27]. Therefore, the range of q was 1.0 ≤ q ≤ 1.1 in the numerical calculations.…”

Section: Numerical Estimationmentioning

confidence: 99%

“…The values of the parameters were adopted by referring the results in high energy collisions. The value of q − 1 > 0 is small in many cases [27]. Therefore, the range of q was 1.0 ≤ q ≤ 1.1 in the numerical calculations.…”

Section: Numerical Estimationmentioning

confidence: 99%

Transverse momentum fluctuation under the Tsallis distribution at high energies

Ishihara

2017

Int. J. Mod. Phys. E

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We studied the effects of the Tsallis distribution on the transverse momentum fluctuation in high energy collisions. The parton-hadron duality and the Bose-Einstein type correlation between partons were assumed. The fluctuation was calculated in the boost-invariant picture for the expectation value used in the Boltzmann-Gibbs statistics and for the expectation value used in the Tsallis nonextensive statistics. It was shown that the fluctuation is a function of η which is the ratio of the inverse temperature to the correlation length. We found the following points: (1) the fluctuation depends on the form of the distribution and depends weakly on the definition of the expectation value used in the statistics, (2) the fluctuation increases as the entropic parameter value of the Tsallis distribution increases, and (3) the variation of the fluctuation as a function of the entropic parameter for the expectation value used in the BoltzmannGibbs statistics is larger than that for the expectation value used in the Tsallis nonextensive statistics in the wide range of η.

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“…Power-like distributions have been used to describe momentum distributions in high energy collisions. Many researchers have studied momentum distributions, and have used Tsallis-type distributions [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. It has been reported that Tsallis-type distributions describe well momentum distributions.…”

Section: Introductionmentioning

confidence: 99%

Chiral phase transition within the linear sigma model in the Tsallis nonextensive statistics based on density operator

Ishihara

2019

Int. J. Mod. Phys. E

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We studied chiral phase transition in the linear sigma model within the Tsallis nonextensive statistics in the case of small deviation from the Boltzmann-Gibbs (BG) statistics. The statistics has two parameters: the temperature T and the entropic parameter q. The normalized q-expectation value and the physical temperature T ph were employed in this study. The normalized q-expectation value was expanded as a series of the value (1 − q), where the absolute value |1 − q| is the measure of the deviation from the BG statistics. We applied the Hartree factorization and the free particle approximation, and obtained the equations for the condensate, the sigma mass, and the pion mass. The physical temperature dependences of these quantities were obtained numerically. We found following facts. The condensate at q is smaller than that at q ′ for q > q ′ . The sigma mass at q is lighter than that at q ′ for q > q ′ at low physical temperature, and the sigma mass at q is heavier than that at q ′ for q > q ′ at high physical temperature. The pion mass at q is heavier than that at q ′ for q > q ′ . The difference between the pion masses at different values of q is small for T ph ≤ 200 MeV. That is to say, the condensate and the sigma mass are affected by the Tsallis nonextensive statistics of small |1 − q|, and the pion mass is also affected by the statistics of small |1 − q| except for T ph ≤ 200 MeV.

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The Parameters of The Tsallis Distribution at the LHC

Cited by 16 publications

References 28 publications

Tsallis-thermometer: a QGP indicator for large and small collisional systems

Tsallis-thermometer: a QGP indicator for large and small collisional systems

Transverse momentum fluctuation under the Tsallis distribution at high energies

Chiral phase transition within the linear sigma model in the Tsallis nonextensive statistics based on density operator

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