Study On The Parton Distributions

It was realized that the nucleon is made of hard point-like objects in the experiment on the charged lepton-hadron deep inelastic scattering at the Stan-ford Linear Accelerator(SLC) at the end of the last sixties,which are called par-tons.Parton distributions are introduced to describe the partons in the kinemat-ics.There are two kinds of parton distributions, integrated and unintegrated par-ton distributions.Integrated parton distributions can be used to describe the in-clusive cross sections in the deep inelastic scattering experiments,the inner struc-ture of nucleon and so on,which fulfill the conventional twist-2 DGLAP evolu-tion equations.The higer-twist effect must be considered when parton densities are big enough that the nuclear shadowing effects are obvious and the evolution equations should be modified to the twist-4 DGLAP.The differences can be found from the two different evolution equations. The transverse momenta of partons can play an important role in some reactions such as Drell-Yan production and the unintegrated parton distributions are introduced which depend not only on one scale.The unintegrated parton distributions can be obtained by the CCFM equa-tion. The solution of the CCFM equation is complicated and only can be obtained with Monte Carlo generator.In this paper the integrated parton distributions are obtained based on the DGLAP equations and the unintegrated parton distribu-tions can be derived from the integrated parton distributions in the KMR scheme. Particle multiplicity distribution detected in the experiments is closely related to the parton distributions and one can predict the conditions at the higher energy.In this paper,we first study the differences of the integrated parton distribu-tions from the twist-2 DGLAP and the twist-4 DGLAP evolution equations.Then we compare the unintegrated parton distributions from twist-2 and twist-4 condi-tion in the KMR scheme.Thirdly,we compute the Dijet correlations and compare the results.Finally,we predict the particle multiplicity distributions.