A fast high-order method to calculate wakefield forces in an electron beam

In this paper we report on a high-order fast method to numerically calculate wakefield forces in an electron beam given a wake function model. This method is based on a Newton-Cotes quadrature rule for integral approximation and an FFT method for discrete summation that results in an $O(Nlog(N))$ computational cost, where $N$ is the number of grid points. Using the Simpson quadrature rule with an accuracy of $O(h^4)$, where $h$ is the grid size, we present numerical calculation of the wakefields from a resonator wake function model and from a one-dimensional coherent synchrotron radiation (CSR) wake model. Besides the fast speed and high numerical accuracy, the calculation using the direct line density instead of the first derivative of the line density avoids numerical filtering of the electron density function for computing the CSR wakefield force

Large-Scale Simulation of Beam Dynamics in High Intensity Ion Linacs Using Parallel Supercomputers

In this paper we present results of using parallel supercomputers to simulate beam dynamics in next-generation high intensity ion linacs. Our approach uses a three-dimensional space charge calculation with six types of boundary conditions. The simulations use a hybrid approach involving transfer maps to treat externally applied fields (including rf cavities) and parallel particle-in-cell techniques to treat the space-charge fields. The large-scale simulation results presented here represent a three order of magnitude improvement in simulation capability, in terms of problem size and speed of execution, compared with typical two-dimensional serial simulations. Specific examples will be presented, including simulation of the spallation neutron source (SNS) linac and the Low Energy Demonstrator Accelerator (LEDA) beam halo experiment

Pseudogap and Fermi-arc Evolution in the Phase-fluctuation Scenario

Pseudogap phenomena and the formation of Fermi arcs in underdoped cuprates are numerically studied in the presence of phase fluctuations that are simulated by an XY model. Most importantly the spectral function for each Monte Carlo sample is calculated directly and efficiently by the Chebyshev polynomials without having to diagonalize the fermion Hamiltonian, which enables us to handle a system large enough to achieve sufficient momentum/energy resolution. We find that the momentum dependence of the energy gap is identical to that of a pure d-wave superconductor well below the KT-transition temperature ($T_{KT}$), while displays an upturn deviation from $\cos k_x - \cos k_y$ with increasing temperature. An abrupt onset of the Fermi arcs is observed above $T_{KT}$ and the arc length exhibits a similar temperature dependence to the thermally activated vortex excitations.Comment: 5 pages, 4 figure

Complex Unitary Recurrent Neural Networks using Scaled Cayley Transform

Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the \textit{vanishing or exploding gradient} problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN) which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training and a hyperparameter is introduced to tune the matrix for each particular task. In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We also provide an analysis of a potential issue of the modReLU activiation function which is used in our work and several other unitary RNNs. In the experiments conducted, the scaled Cayley unitary recurrent neural network (scuRNN) achieves comparable or better results than scoRNN and other unitary RNNs without fixing the scaling matrix

Crossing of Phantom Divide in F(R)F(R) Gravity

An explicit model of $F(R)$ gravity with realizing a crossing of the phantom divide is reconstructed. In particular, it is shown that the Big Rip singularity may appear in the reconstructed model of $F(R)$ gravity. Such a Big Rip singularity could be avoided by adding $R^2$ term or non-singular viable $F(R)$ theory to the model because phantom behavior becomes transient.Comment: 9 pages, 1 figure, to be published in the proceedings of the International Workshop on Dark Matter, Dark Energy and Matter-antimatter Asymmetry in Special Issue of Modern Physics Letters A, Department of Physics, National Tsing Hua University, Hsinchu, Taiwan, 20th - 21st November, 200

TGA/FTIR Studies on the Thermal Degradation of some Polymeric Sulfonic and Phosphonic Acids and Their Sodium Salts

The thermal degradation of poly(vinyl sulfonic acid) and its sodium salt, poly(4-styrenesulfonic acid) and its sodium salt, and poly(vinylphosphonic acid) was studied by a combination of techniques, including TGA/FTIR, to identify the volatile products which were evolved during the degradation as well as analysis of the residues which were obtained in order to propose a mechanism for the degradation. The motivation for the work was to attempt to identify new monomers which could be graft copolymerized onto a polymer in order to improve the thermal stability of that polymer