We propose QCD inspired model to calculate ^-pp and pp elastic scatterings at high energies in this paper. A calculation for total cross section of ^-pp and pp is performed in which the contributions from gluon-gluon,...We propose QCD inspired model to calculate ^-pp and pp elastic scatterings at high energies in this paper. A calculation for total cross section of ^-pp and pp is performed in which the contributions from gluon-gluon, quark-quark, and gluon-quark interactions are included. Our results show that the QCD inspired model gives a perfect fit to experimental data of total cross section both for ^-pp and pp elastic scatterings at the whole energy region where experimental data existed at FNAL and CERN.展开更多
We use the QCD inspired model to analyze the ratio of the real to the imaginary for pp and pp elastic scatterings. A calculation for the ratio of the real to the imaginary is performed in which the contributions from ...We use the QCD inspired model to analyze the ratio of the real to the imaginary for pp and pp elastic scatterings. A calculation for the ratio of the real to the imaginary is performed in which the contributions from gluongluon interaction, quark-quark interaction, quark-gluon interaction, and odd eikonal profile function are included. Our results show that the QCD inspired model gives a good fit to the LHC experimental data.展开更多
Based on the quark-gluon structure of nucleon and the possible existence of Odderon in strong interaction process due to gluon self-interaction, the elastic scatterings of pp and p^-p at high energies are studied. The...Based on the quark-gluon structure of nucleon and the possible existence of Odderon in strong interaction process due to gluon self-interaction, the elastic scatterings of pp and p^-p at high energies are studied. The contributions from individual terms of quark-quark, gluon-gluon interactions, quark-gluon interference, and the Odderon terms to the nuclear slope parameter B(s) are analyzed. Our results show that the QCD inspired model gives a good fit to the LHC experimental data of the nuclear slope parameter.展开更多
Based on the generalized vector meson dominance model in QCD, we study photoproduction of vector meson T off the proton by use of the QCD inspired model in which the contributions from quark-quark, gluon-gluon, and qu...Based on the generalized vector meson dominance model in QCD, we study photoproduction of vector meson T off the proton by use of the QCD inspired model in which the contributions from quark-quark, gluon-gluon, and quark-gluon interference term to observable are taken into consideration. Calculations are performed for total cross section σtot, differential cross section dσ/dt, ratio of the real part to imaginary part of forward scattering amplitude ρ, and nuclear slop parameter function β. The mediators of interactions between projectiles (the quark and antiquark pair fluctuated from the real the photon) and the proton target (three-quark system) are the tensor Glueball and Odderon instead of using the usual Pomeron exchange. The theoretical predictions for σtot (s) are consistent with the experimental data within error bars of the data. The data for dσ/dt, ρ, and β are urgently needed.展开更多
By means of the UGD function extracted from an AdS/CFT inspired saturation model, the charm and bottom structure functions are studied in fixed-order perturbation theory. It is shown that the theoretical results are i...By means of the UGD function extracted from an AdS/CFT inspired saturation model, the charm and bottom structure functions are studied in fixed-order perturbation theory. It is shown that the theoretical results are in good agreement with the recent HERA data. Then, this UGD function is also used to investigate net-kaon rapidity distribution in Au+Au collisions at RHIC energies and the theoretical results fit well to the BRAHMS data. In the end of this paper, we give the predicted results for nuclear charm structure function at very small x where the popular shadowing parameterizations are invalid.展开更多
Recent decades have witnessed a much increased demand for advanced,effective and efficient methods and tools for analyzing,understanding and dealing with data of increasingly complex,high dimensionality and large volu...Recent decades have witnessed a much increased demand for advanced,effective and efficient methods and tools for analyzing,understanding and dealing with data of increasingly complex,high dimensionality and large volume.Whether it is in biology,neuroscience,modern medicine and social sciences or in engineering and computer vision,data are being sampled,collected and cumulated in an unprecedented speed.It is no longer a trivial task to analyze huge amounts of high dimensional data.A systematic,automated way of interpreting data and representing them has become a great challenge facing almost all fields and research in this emerging area has flourished.Several lines of research have embarked on this timely challenge and tremendous progresses and advances have been made recently.Traditional and linear methods are being extended or enhanced in order to meet the new challenges.This paper elaborates on these recent advances and discusses various state-of-the-art algorithms proposed from statistics,geometry and adaptive neural networks.The developments mainly follow three lines:multidimensional scaling,eigen-decomposition as well as principal manifolds.Neural approaches and adaptive or incremental methods are also reviewed.In the first line,traditional multidimensional scaling(MDS)has been extended not only to be more adaptive such as neural scale,curvilinear component analysis(CCA)and visualization induced self-organizing map(ViSOM)for online learning,but also to be more local scaling such as Isomap for enhanced flexibility for nonlinear data sets.The second line extends linear principal component analysis(PCA)and has attracted a huge amount of interest and enjoyed flourishing advances with methods like kernel PCA(KPCA),locally linear embedding(LLE)and Laplacian eigenmap.The advantage is obvious:a nonlinear problem is transformed into a linear one and a unique solution can then be sought.The third line starts with the nonlinear principal curve and surface and links up with adaptive neural network approaches such as self-organizing map(SOM)and ViSOM.Many of these frameworks have been further improved and enhanced for incremental learning and mapping function generalization.This paper discusses these recent advances and their connections.Their application issues and implementation matters will also be briefly enlightened and commented on.展开更多
Based on the generalized QCD vector meson dominance model, we study the electroproduction of a vector meson off a proton in the QCD inspired eikonalized model. Numerical calculations for the total cross section σ tot...Based on the generalized QCD vector meson dominance model, we study the electroproduction of a vector meson off a proton in the QCD inspired eikonalized model. Numerical calculations for the total cross section σ tot and differential cross section dσ/dt are performed for ρ, ω and φ meson electroproduction in this paper. Since gluons interact among themselves (self-interaction), two gluons can form a glueball with quantum numbers I G , J P C = 0 + , 2 ++ , decay width Γ t ≈ 100 MeV, and mass of m G =2.23 GeV. The three gluons can form a three-gluon colorless bound state with charge conjugation quantum number C = 1, called the Odderon. The mediators of interactions between projectiles (the quark and antiquark pair fluctuated from the virtual photon) and the proton target (a three-quark system) are the tensor glueball and the Odderon. Our calculated results in the tensor glueball and Odderon exchange model fit to the existing data successfully, which evidently shows that our present QCD mechanism is a good description of meson electroproduction off a proton. It should be emphasized that our mechanism is different from the theoretical framework of Block et al. We also believe that the present study and its success are important for the investigation of other vector meson electro- and photoproduction at high energies, as well as for searching for new particles such as tensor glueballs and Odderons, which have been predicted by QCD and the color glass condensate model (CGC). Therefore, in return, it can test the validity of QCD and the CGC model.展开更多
基金The project supported in part by National Natural Science Foundation of China under Grant Nos. 10647002 and 10565001 and the Science Foundation of Guangxi Province of China under Grant Nos. 0481030, 0542042, and 0575020
文摘We propose QCD inspired model to calculate ^-pp and pp elastic scatterings at high energies in this paper. A calculation for total cross section of ^-pp and pp is performed in which the contributions from gluon-gluon, quark-quark, and gluon-quark interactions are included. Our results show that the QCD inspired model gives a perfect fit to experimental data of total cross section both for ^-pp and pp elastic scatterings at the whole energy region where experimental data existed at FNAL and CERN.
基金The project supported in part by National Natural Science Foundation of China under Grant Nos. 10647002 and 10565001 and the Science Foundation of Guangxi Province of China under Grant Nos. 0481030, 0542042, and 0575020
文摘We use the QCD inspired model to analyze the ratio of the real to the imaginary for pp and pp elastic scatterings. A calculation for the ratio of the real to the imaginary is performed in which the contributions from gluongluon interaction, quark-quark interaction, quark-gluon interaction, and odd eikonal profile function are included. Our results show that the QCD inspired model gives a good fit to the LHC experimental data.
基金National Natural Science Foundation of China under Grant Nos.10565001 and 10647002the Natural Science Foundation of Guangxi Province of China under Grant Nos.0575020,0542042,and 0481030Guangxi University under Grant No.X051001
文摘Based on the quark-gluon structure of nucleon and the possible existence of Odderon in strong interaction process due to gluon self-interaction, the elastic scatterings of pp and p^-p at high energies are studied. The contributions from individual terms of quark-quark, gluon-gluon interactions, quark-gluon interference, and the Odderon terms to the nuclear slope parameter B(s) are analyzed. Our results show that the QCD inspired model gives a good fit to the LHC experimental data of the nuclear slope parameter.
基金Supported by National Natural Science Foundation of China under Grant Nos.10647002,10565001the Guangxi Natural Science Foundation under Grant Nos.0575020,0542042,and 0481030
文摘Based on the generalized vector meson dominance model in QCD, we study photoproduction of vector meson T off the proton by use of the QCD inspired model in which the contributions from quark-quark, gluon-gluon, and quark-gluon interference term to observable are taken into consideration. Calculations are performed for total cross section σtot, differential cross section dσ/dt, ratio of the real part to imaginary part of forward scattering amplitude ρ, and nuclear slop parameter function β. The mediators of interactions between projectiles (the quark and antiquark pair fluctuated from the real the photon) and the proton target (three-quark system) are the tensor Glueball and Odderon instead of using the usual Pomeron exchange. The theoretical predictions for σtot (s) are consistent with the experimental data within error bars of the data. The data for dσ/dt, ρ, and β are urgently needed.
基金Supported by Natural Science Foundation of Hebei Province(A2012210043)
文摘By means of the UGD function extracted from an AdS/CFT inspired saturation model, the charm and bottom structure functions are studied in fixed-order perturbation theory. It is shown that the theoretical results are in good agreement with the recent HERA data. Then, this UGD function is also used to investigate net-kaon rapidity distribution in Au+Au collisions at RHIC energies and the theoretical results fit well to the BRAHMS data. In the end of this paper, we give the predicted results for nuclear charm structure function at very small x where the popular shadowing parameterizations are invalid.
文摘Recent decades have witnessed a much increased demand for advanced,effective and efficient methods and tools for analyzing,understanding and dealing with data of increasingly complex,high dimensionality and large volume.Whether it is in biology,neuroscience,modern medicine and social sciences or in engineering and computer vision,data are being sampled,collected and cumulated in an unprecedented speed.It is no longer a trivial task to analyze huge amounts of high dimensional data.A systematic,automated way of interpreting data and representing them has become a great challenge facing almost all fields and research in this emerging area has flourished.Several lines of research have embarked on this timely challenge and tremendous progresses and advances have been made recently.Traditional and linear methods are being extended or enhanced in order to meet the new challenges.This paper elaborates on these recent advances and discusses various state-of-the-art algorithms proposed from statistics,geometry and adaptive neural networks.The developments mainly follow three lines:multidimensional scaling,eigen-decomposition as well as principal manifolds.Neural approaches and adaptive or incremental methods are also reviewed.In the first line,traditional multidimensional scaling(MDS)has been extended not only to be more adaptive such as neural scale,curvilinear component analysis(CCA)and visualization induced self-organizing map(ViSOM)for online learning,but also to be more local scaling such as Isomap for enhanced flexibility for nonlinear data sets.The second line extends linear principal component analysis(PCA)and has attracted a huge amount of interest and enjoyed flourishing advances with methods like kernel PCA(KPCA),locally linear embedding(LLE)and Laplacian eigenmap.The advantage is obvious:a nonlinear problem is transformed into a linear one and a unique solution can then be sought.The third line starts with the nonlinear principal curve and surface and links up with adaptive neural network approaches such as self-organizing map(SOM)and ViSOM.Many of these frameworks have been further improved and enhanced for incremental learning and mapping function generalization.This paper discusses these recent advances and their connections.Their application issues and implementation matters will also be briefly enlightened and commented on.
基金Supported by Guangxi Science Foundation for Young Researchers(0991009)Department of Guangxi Education(200807MS112)+1 种基金Department of Guangxi Education for the Excellent scholars of Higher Education(2011-54)Doctoral Science Foundation of Guangxi University of Technology(11Z16)
文摘Based on the generalized QCD vector meson dominance model, we study the electroproduction of a vector meson off a proton in the QCD inspired eikonalized model. Numerical calculations for the total cross section σ tot and differential cross section dσ/dt are performed for ρ, ω and φ meson electroproduction in this paper. Since gluons interact among themselves (self-interaction), two gluons can form a glueball with quantum numbers I G , J P C = 0 + , 2 ++ , decay width Γ t ≈ 100 MeV, and mass of m G =2.23 GeV. The three gluons can form a three-gluon colorless bound state with charge conjugation quantum number C = 1, called the Odderon. The mediators of interactions between projectiles (the quark and antiquark pair fluctuated from the virtual photon) and the proton target (a three-quark system) are the tensor glueball and the Odderon. Our calculated results in the tensor glueball and Odderon exchange model fit to the existing data successfully, which evidently shows that our present QCD mechanism is a good description of meson electroproduction off a proton. It should be emphasized that our mechanism is different from the theoretical framework of Block et al. We also believe that the present study and its success are important for the investigation of other vector meson electro- and photoproduction at high energies, as well as for searching for new particles such as tensor glueballs and Odderons, which have been predicted by QCD and the color glass condensate model (CGC). Therefore, in return, it can test the validity of QCD and the CGC model.