The glueball-to-vacuum matrix elements of local gluonic operators in scalar, tensor, and pseudoscalar channels are investigated numerically on several anisotropic lattices with the spatial lattice spacing ranging from 0.1fm -0.2fm. These matrix elements are needed to predict the glueball branching ratios in J/ψ radiative decays which will help identify the glueball states in experiments. Two types of improved local gluonic operators are constructed for a self-consistent check and the finite volume effects are studied. We find that lattice spacing dependence of our results is very weak and the continuum limits are reliably extrapolated, as a result of improvement of the lattice gauge action and local operators. We also give updated glueball masses with various quantum numbers.
Using the Euclidean path-integral formulation for the hadronic tensor, we show that the violation of the Gottfried sum rule does not come from the disconnected quark-loop insertion. Rather, it comes from the connected (quark line) insertion involving quarks propagating in the backward time direction. We demonstrate this by studying sum rules in terms of the scalar and axialvector matrix elements in lattice gauge calculations. The effects of eliminating backward time propagation are presented.
Resampling raw surface meshes is one of the most fundamental operations used by nearly all digital geometry processing systems. The vast majority of this work has focused on triangular remeshing, yet quadrilateral meshes are preferred for many surface PDE problems, especially fluid dynamics, and are best suited for defining Catmull-Clark subdivision surfaces. We describe a fundamentally new approach to the quadrangulation of manifold polygon meshes using Laplacian eigenfunctions, the natural harmonics of the surface. These surface functions distribute their extrema evenly across a mesh, which connect via gradient flow into a quadrangular base mesh. An iterative relaxation algorithm simultaneously refines this initial complex to produce a globally smooth parameterization of the surface. From this, we can construct a well-shaped quadrilateral mesh with very few extraordinary vertices. The quality of this mesh relies on the initial choice of eigenfunction, for which we describe algorithms and hueristics to efficiently and effectively select the harmonic most appropriate for the intended application.
We report on a lattice QCD calculation of the strangeness magnetic moment of the nucleon. Our result is G s M (0) = −0.36 ± 0.20. The sea contributions from the u and d quarks are about 80% larger. However, they cancel to a large extent due to their electric charges, resulting in a smaller net sea contribution of −0.097 ± 0.037µ N to the nucleon magnetic moment. As far as the neutron to proton magnetic moment ratio is concerned, this sea contribution tends to cancel out the cloud-quark effect from the Z-graphs and result in a ratio of −0.68 ± 0.04 which is close to the SU(6) relation and the experiment. The strangeness Sachs electric mean-square radius r 2 s E is found to be small and negative and the total sea contributes substantially to the neutron electric form factor.
We calculate the quark orbital angular momentum of the nucleon from the quark energy-momentum tensor form factors on the lattice with the quenched approximation. The disconnected insertion is estimated stochastically which employs the Z 2 noise with an unbiased subtraction. This reduced the error by a factor of 3-4 with negligible overhead. The total quark contribution to the proton spin is found to be 0.30Ϯ0.07. From this and the quark spin content we deduce the quark orbital angular momentum to be 0.17Ϯ0.06 which is ϳ34% of the proton spin. We further predict that the gluon angular momentum is 0.20Ϯ0.07; i.e., ϳ40% of the proton spin is due to the glue.
We introduce a Z 2 noise for the stochastic estimation of matrix inversion and discuss its superiority over other noises including the Gaussian noise. This algorithm is applied to the calculation of quark loops in lattice quantum chromodynamics that involves diagonal and off-diagonal traces of the inverse matrix. We will point out its usefulness in its applications to estimating determinants, eigenvalues, and eigenvectors, as well as its limitations based on the structure of the inverse matrix.
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