Multiplicity distribution at high energies

1985 ◽  
Vol 63 (7) ◽  
pp. 954-955 ◽  
Author(s):  
S. H. Ling ◽  
K. Young
Keyword(s):  
Multiplicity Distribution ◽  
Broad Class ◽  
Geometric Models ◽  
High Energies

The break in the multiplicity distribution at [Formula: see text] in the [Formula: see text] collider is explained in a broad class of geometric models. The break is predicted to move slowly to higher z as energy increases.

A POSSIBLE DESCRIPTION OF MULTIPLICITY DISTRIBUTION AT HIGH ENERGIES

1992 ◽  
Vol 07 (34) ◽  
pp. 3211-3214
Author(s):  
ISAY GOLYAK
Keyword(s):  
Multiplicity Distribution ◽  
Charged Particles ◽  
High Energies ◽  
Negative Binomials

It is shown that the parameters of two negative binomials describing the multiplicity distribution of secondary charged particles are connected with the mechanisms of generations and decays of two-body resonances.

Tsallis nonextensive entropy and the multiplicity distributions in high energy leptonic collisions

2016 ◽  
Vol 25 (06) ◽  
pp. 1650041 ◽  
Author(s):  
S. Sharma ◽  
M. Kaur ◽  
Sandeep Kaur
Keyword(s):  
Experimental Data ◽  
Multiplicity Distribution ◽  
High Energy ◽  
New Approach ◽  
High Energies ◽  
Nonextensive Entropy ◽  
Tsallis Distribution ◽  
Multiplicity Distributions ◽  
Better Than

The nonextensive behavior of entropy is exploited to explain the regularity in multiplicity distributions in [Formula: see text] collisions at high energies. The experimental data are analyzed by using Tsallis [Formula: see text]-statistics. We propose a new approach of applying Tsallis [Formula: see text]-statistics, wherein the multiplicity distribution is divided into two components; two-jet and multijet components. A convoluted Tsallis distribution is fitted to the data. It is shown that this method gives the best fits which are several orders better than the conventional fit of Tsallis distribution.

A STUDY OF K–P INTERACTION AT HIGH ENERGY USING ADAPTIVE FUZZY INFERENCE SYSTEM INTERACTIONS

2004 ◽  
Vol 15 (07) ◽  
pp. 1013-1020 ◽  
Author(s):  
M. Y. EL-BAKRY
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference System ◽  
Multiplicity Distribution ◽  
Fuzzy Inference ◽  
Good Alternative ◽  
High Energy ◽  
Adaptive Network ◽  
Inference System ◽  
High Energies ◽  
Charged Pions

Adaptive Network Fuzzy Inference System (ANFIS) is an artificial intelligence (AI)-based technique that proved efficient in a variety of problems such as classification, recognition and modeling of complex systems. This paper utilizes the adaptive network fuzzy inference system to model the K–P interactions. The ANFIS-based K–P model simulates the multiplicity distribution of charged pions at different high energies. The results showed very accurate fitting to the experimental data recommending it to be a good alternative to other theoretical techniques.

A CONNECTION OF INELASTICITY WITH MULTIPLICITY DISTRIBUTION AT HIGH ENERGIES

1992 ◽  
Vol 07 (26) ◽  
pp. 2401-2406 ◽  
Author(s):  
ISAY GOLYAK
Keyword(s):  
Multiplicity Distribution ◽  
Secondary Particles ◽  
High Energies ◽  
The Mean ◽  
Multiplicity Distributions ◽  
Scaling Invariant

The widely known experimental value of the mean coefficient of the inelasticity <K>~0.5 is calculated by the investigation of a connection of the inelasticity with KNO scaling invariant multiplicity distributions of secondary particles.

Multiparticle production in proton–proton collisions at high energies

1985 ◽  
Vol 63 (11) ◽  
pp. 1449-1452 ◽  
Author(s):  
M. T. Hussein ◽  
N. M. Hassan ◽  
M. K. Hegab
Keyword(s):  
Monte Carlo ◽  
Theoretical Model ◽  
Phase Space ◽  
Multiplicity Distribution ◽  
Proton Proton ◽  
Sequential Decay ◽  
Proton Collisions ◽  
High Energies ◽  
Proton Proton Collisions ◽  
Phase Space Integral

The multiplicity distribution and the inelasticity coefficient are studied for proton–proton collisions at incident energies of 6, 19, 25, and 69 GeV. A theoretical model is proposed, based on a sequential decay system formed by the two interacting protons. The Monte Carlo technique is used to calculate the phase-space integral and for simulation of events for the different reactions.

Multifractal Crossover in the Multiplicative Processes

Fractals ◽  
1997 ◽  
Vol 05 (04) ◽  
pp. 661-663
Author(s):  
A. Bershadskii
Keyword(s):  
Experimental Data ◽  
Multiplicity Distribution ◽  
Effective Capacity ◽  
Recent Experimental Data ◽  
Space Filling ◽  
High Energies ◽  
Capacity Dimension ◽  
Electron Positron Annihilation ◽  
Electron Positron ◽  
Multiplicative Processes

It is shown, using multifractal data on space-filling bearings, that there exists a multifractal crossover to an effective 'capacity' dimension in some multiplicative processes. This result is then used to interpret recent experimental data on multifractal structure of multiplicity distribution in electron-positron annihilation at high energies.

Valence electron spectroscopy of inhomogeneous media

1992 ◽  
Vol 50 (2) ◽  
pp. 1150-1151
Author(s):  
A. Howie ◽  
D.W. McComb
Keyword(s):  
Specific Gravity ◽  
Loss Function ◽  
Electron Spectroscopy ◽  
Valence Electron ◽  
Peak Height ◽  
Loss Functions ◽  
Loss Peak ◽  
High Energies ◽  
Valence Electron Density ◽  
Electron Microscopist

The bulk loss function Im(-l/ε (ω)), a well established tool for the interpretation of valence loss spectra, is being progressively adapted to the wide variety of inhomogeneous samples of interest to the electron microscopist. Proportionality between n, the local valence electron density, and ε-1 (Sellmeyer's equation) has sometimes been assumed but may not be valid even in homogeneous samples. Figs. 1 and 2 show the experimentally measured bulk loss functions for three pure silicates of different specific gravity ρ - quartz (ρ = 2.66), coesite (ρ = 2.93) and a zeolite (ρ = 1.79). Clearly, despite the substantial differences in density, the shift of the prominent loss peak is very small and far less than that predicted by scaling e for quartz with Sellmeyer's equation or even the somewhat smaller shift given by the Clausius-Mossotti (CM) relation which assumes proportionality between n (or ρ in this case) and (ε - 1)/(ε + 2). Both theories overestimate the rise in the peak height for coesite and underestimate the increase at high energies.

Contrast From Point Defects in The Infinitesimal Approximation

1980 ◽  
Vol 38 ◽  
pp. 350-351
Author(s):  
L. J. Sykes ◽  
J. J. Hren
Keyword(s):  
Electron Microscope ◽  
Point Defects ◽  
Broad Class ◽  
Stacking Fault ◽  
Crystalline Solids ◽  
Dislocation Loops ◽  
Analytical Expression ◽  
Stacking Fault Tetrahedra

In electron microscope studies of crystalline solids there is a broad class of very small objects which are imaged primarily by strain contrast. Typical examples include: dislocation loops, precipitates, stacking fault tetrahedra and voids. Such objects are very difficult to identify and measure because of the sensitivity of their image to a host of variables and a similarity in their images. A number of attempts have been made to publish contrast rules to help the microscopist sort out certain subclasses of such defects. For example, Ashby and Brown (1963) described semi-quantitative rules to understand small precipitates. Eyre et al. (1979) published a catalog of images for BCC dislocation loops. Katerbau (1976) described an analytical expression to help understand contrast from small defects. There are other publications as well.

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