Transverse momentum spectra of strange hadrons within extensive and nonextensive statistics

Abstract

Using generic (non)extensive statistics, in which the underlying system likely autonomously manifests its extensive and nonextensive statistical nature, we extract various fit parameters from the CMS experiment and compare these to the corresponding results obtained from Tsallis and Boltzmann statistics. The present study is designed to indicate the possible variations between the three types of statistical approaches and characterizes their dependence on collision energy, multiplicity, and size of the system of interest. We analyze the transverse momentum spectra of the strange hadrons , Λ, and Ξ− produced in collisions, at TeV, in collisions, at TeV, and in collisions, at TeV. From the comparison of the resulting fit parameters; temperature T, volume V, and nonextensvie parameter d, with calculations based on Tsallis and Boltzmann statistics, remarkable differences between the three types of statistics are determined besides a strong dependence on size and type of the colliding system. We conclude that the produced particles with large masses and large strange quantum numbers likely freeze out earlier than the ones with smaller masses and less strange quantum numbers. This conclusion seems not depending on the type of the particle or the collision types but apparently manifesting transitions from chemical (larger temperature) to the kinetic freezeouts (lower temperature). For the first universality (equivalent) class , the decrease in the second equivalent class d with increasing energy and collision’s centrality highlights that the system departs from nonextensivity (non-equilibrium) and apparently approaches extensivity (equilibrium) indicating that the Boltzmann statistics becomes the proper statistical approach in describing that system. Last but not least, we present analytical expressions for the energy dependence of the various fit parameters.

Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies

“…To estimate the entropy S, extrapolation of the observed transverse momentum spectra to p T = 0 is required. To achieve this, we fitted both the experimental and simulated p T spectra to two various functional models, Tsallis distribution [11,12] and the HRG model [13]. The aim of using two different models is to fit the whole p T curve.…”

“…Best performance value and regression are obtained by using four hidden layers. The number of neurons in each hidden layer are (100, 100, 120, 120), (70, 90, 80, 80), (100, 80, 80, 70), (20,30,30,20), (30,20,40,40) Extrapolation of the observed transverse momentum spectra to p T = 0 is necessary to determine the entropy S. To achieve this, we fitted both the experimental and simulated p T spectra to two various functional models, Tsallis distribution [11,12] and the HRG model [13]. The aim of combining two models is to fit the entire p T curve.…”

“…The present work aims to calculate the entropy per rapidity ds/dy based on the transverse momentum distribution measured in Pb-Pb collisions at √ s = 2.76, and 5.02 TeV for particles π, k, p, Λ, Ω, and Σ, and π, k, p, Λ, and K 0 s , respectively. For a precise estimation of the entropy per rapidity ds/dy, we fitted the transverse momentum distribution of the considered particles using two thermal approaches, Tsalis distribution [11,12] and the HRG model [13]. This enable us to cover a large range of the measured transverse particle momentum p T , up to ∼ 20 GeV/c, unlike hanus that used a small range of p T , ∼ 1.5 GeV/c and consider the particle's mass as free parameter.…”

Nonextensive hydrodynamics of boost-invariant plasmas

“…Tsallis statistics has been used to fit the spectra more accurately over a wide range of p T in many high-energy experiments involving different systems and different collision energies, [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Rather than performing a direct fit to the data using a Tsallis distribution for the freeze-out hypersurface distribution, one would like to account for the nonextensivity in the underlying dynamical model, using Tsallis non-extensive statistics.…”

Model studies of V0 production ratios in $pp$ collisions at $\sqrt{\mathrm{s}}$ = 0.2, 0.9, and 7 TeV

“…A deconfined, strongly interacting state of matter, called Quark-Gluon plasma (QGP) is conjectured to be formed, at high temperatures and densities, in collisions recorded at high energy physics experiments. The formation of QGP is gauged with a number of proposed signatures including strangeness enhancement, and the transverse momentum (p T ) spectra of strange particles play an important role in the determination of freezeout parameters [1][2][3]. Strange hadrons production in relativistic high energy collisions is an invaluable tool to investigate the properties of collision phases, since these are not part of the colliding nuclei from the incoming beams.…”