Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEquivalence of the Siegert-pseudostate and Lagrange-mesh R-matrix methods
54
0
0.0
(
0
)
Added by
Jean-Marc Sparenberg
Publication date
2002
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Siegert pseudostates are purely outgoing states at some fixed point expanded over a finite basis. With discretized variables, they provide an accurate description of scattering in the s wave for short-range potentials with few basis states. The R-matrix method combined with a Lagrange basis, i.e. functions which vanish at all points of a mesh but one, leads to simple mesh-like equations which also allow an accurate description of scattering. These methods are shown to be exactly equivalent for any basis size, with or without discretization. The comparison of their assumptions shows how to accurately derive poles of the scattering matrix in the R-matrix formalism and suggests how to extend the Siegert-pseudostate method to higher partial waves. The different concepts are illustrated with the Bargmann potential and with the centrifugal potential. A simplification of the R-matrix treatment can usefully be extended to the Siegert-pseudostate method.
rate research
Read More
The Lagrange-mesh $R$-matrix method is generalized to inhomogeneous equations. This method is numerically stable and efficient. It can be directly used for transfer reactions with the formalism discussed by Ascuitto and Glendenning [Phys. Rev. 181,1396 (1969)] and for inclusive breakup reactions modeled by Ichimura, Austern, and Vincent [Phys. Rev. C 32, 431 (1985)]. We first present a simple example to assess the method. Then the application to the $^{93}$Nb($d$,$pX$) non-elastic breakup is discussed.
Relativistic dipolar to hexadecapolar polarizabilities of the ground state and some excited states of hydrogenic atoms are calculated by using numerically exact energies and wave functions obtained from the Dirac equation with the Lagrange-mesh method. This approach is an approximate variational method taking the form of equations on a grid because of the use of a Gauss quadrature approximation. The partial polarizabilities conserving the absolute value of the quantum number $kappa$ are also numerically exact with small numbers of mesh points. The ones where $|kappa|$ changes are very accurate when using three different meshes for the initial and final wave functions and for the calculation of matrix elements. The polarizabilities of the $n=2$ excited states of hydrogenic atoms are also studied with a separate treatment of the final states that are degenerate at the nonrelativistic approximation. The method provides high accuracies for polarizabilities of a particle in a Yukawa potential and is applied to a hydrogen atom embedded in a Debye plasma.
Radiative transfer has a strong impact on the collapse and the fragmentation of prestellar dense cores. We present the radiation-hydrodynamics solver we designed for the RAMSES code. The method is designed for astrophysical purposes, and in particular for protostellar collapse. We present the solver, using the co-moving frame to evaluate the radiative quantities. We use the popular flux limited diffusion approximation, under the grey approximation (one group of photon). The solver is based on the second-order Godunov scheme of RAMSES for its hyperbolic part, and on an implicit scheme for the radiation diffusion and the coupling between radiation and matter. We report in details our methodology to integrate the RHD solver into RAMSES. We test successfully the method against several conventional tests. For validation in 3D, we perform calculations of the collapse of an isolated 1 M_sun prestellar dense core, without rotation. We compare successfully the results with previous studies using different models for radiation and hydrodynamics. We have developed a full radiation hydrodynamics solver in the RAMSES code, that handles adaptive mesh refinement grids. The method is a combination of an explicit scheme and an implicit scheme, accurate to the second-order in space. Our method is well suited for star formation purposes. Results of multidimensional dense core collapse calculations with rotation are presented in a companion paper.
The present debate on the reliability of astrophysical opacities has reached a new climax with the recent measurements of Fe opacities on the Z-machine at the Sandia National Laboratory citep{Bailey2015}. To understand the differences between theoretical results, on the one hand, and experiments on the other, as well as the differences among the various theoretical results, detailed comparisons are needed. Many ingredients are involved in the calculation of opacities; deconstructing the whole process and comparing the differences at each step are necessary to quantify their importance and impact on the final results. We present here such a comparison using the two main approaches to calculate the required atomic data, the $R$-Matrix and distorted-wave methods, as well as sets of configurations and coupling schemes to quantify the effects on the opacities for the $Fe XVII$ and $Ni XIV$ ions.
Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world. Concurrently, advances in 3D shape prediction have mostly focused on synthetic benchmarks and isolated objects. We unify advances in these two areas. We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. Our system, called Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch that outputs meshes with varying topological structure by first predicting coarse voxel representations which are converted to meshes and refined with a graph convolution network operating over the meshs vertices and edges. We validate our mesh prediction branch on ShapeNet, where we outperform prior work on single-image shape prediction. We then deploy our full Mesh R-CNN system on Pix3D, where we jointly detect objects and predict their 3D shapes.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
