Simulation of waviness in neutron guides

As the trend of neutron guide designs points towards longer and more complex guides, imperfections such as waviness becomes increasingly important. Simulations of guide waviness has so far been limited by a lack of reasonable waviness models. We here present a stochastic description of waviness and its implementation in the McStas simulation package. The effect of this new implementation is compared to the guide simulations without waviness and the simple, yet unphysical, waviness model implemented in McStas 1.12c and 2.0

Effects of ground movements on realistic guide models for the European Spallation Source

We model the effect of ground movement, based on empirical experience, on the transport properties of long neutron guides by ray-tracing simulations. Our results reproduce the large losses found by an earlier study for a simple model, while for a more realistic engineering model of guide mounting, we find the losses to be significantly smaller than earlier predicted. A detailed study of the guide for the cold neutron spectrometer BIFROST at the European Spallation Source shows that the loss is 7.0(5) % for wavelengths of 2.3-4.0 {\AA}; the typical operational wavelength range of the instrument. This amount of loss does not call for mitigation by overillumination as suggested in the previous work. Our work serves to quantify the robustness of the transport properties of long neutron guides, in construction or planning at neutron facilities worldwide.Comment: 8 pages, 12 figure

A Comparison of Approximation Algorithms for the MaxCut-Problem

In this paper we compare, from a practical point of view, approximation algorithms for the problem MaxCut . For this problem, we are given an undirected graph G = (V;E) with vertex set V and edge set E, and we are looking for a partition V = V1 [ V2 with V1 \ V2 = 0 of the vertex set which maximizes the number of edges e 2 E which have one endpoint in V1 and the other in V2 . The investigated algorithms include semidefinite programming, a random strategy, genetic algorithms, two combinatorial algorithms and a divide-and-conquer strategy

Effective Hamiltonian and low-lying energy clustering patterns of four-sublattice antiferromagnets

We study the low-lying energy clustering patterns of quantum antiferromagnets with p sublattices (in particular p=4). We treat each sublattice as a large spin, and using second-order degenerate perturbation theory, we derive the effective (biquadratic) Hamiltonian coupling the p large spins. In order to compare with exact diagonalizations, the Hamiltonian is explicitly written for a finite-size lattice, and it contains information on energies of excited states as well as the ground state. The result is applied to the face-centered-cubic Type I antiferromagnet of spin 1/2, including second-neighbor interactions. A 32-site system is exactly diagonalized, and the energy spectrum of the low-lying singlets follows the analytically predicted clustering pattern.Comment: 17 pages, 4 table

Valence bond glass on an fcc lattice in the double perovskite Ba2YMoO6

Peer reviewedPublisher PD

User and programmers guide to the neutron ray-tracing package McStas, version 1.2

The software package McStas is a tool for writing Monte Carlo ray-tracing simulations of neutron scattering instruments with very high complexity and precision. The simulations can compute all aspects of the performance of instruments and can thus be usedto optimize the use of existing equipment as well as the design of new instrumentation. McStas is based on a unique design where an automatic compilation process translates high-level textual instrument descriptions into efficient ANSI C code. Thisdesign makes it simple to set up typical simulations and also give essentially unlimited freedom to handle more unusual needs. This report constitutes the reference manual for McStas, and contains full documentation for all ascpects of the program. Itcovers the various ways to compile and run simulations; a description of the metalanguage used to define simulations; a full description of all algorithms used to calculate the effects of the various optical components in instruments; and some examplesimulations performed with the program.The software package McStas is a tool for writing Monte Carlo ray-tracing simulations of neutron scattering instruments with very high complexity and precision. The simulations can compute all aspects of the performance of instruments and can thus be used to optimize the use of existing equipment as well as the design of new instrumentation. McStas is based on a unique design where an automatic compilation process translates high-level textual instrument descriptions into efficient ANSI C code. This design makes it simple to set up typical simulations and also give essentially unlimited freedom to handle more unusual needs. This report constitutes the reference manual for McStas, and contains full documentation for all ascpects of the program. It covers the various ways to compile and run simulations; a description of the metalanguage used to define simulations; a full description of all algorithms used to calculate the effects of the various optical components in instruments; and some example simulations performed with the program

MOD_p-tests, Almost Independence and Small Probability Spaces

In this paper, we consider approximations of probability distributions over ZZ n p . We present an approach to estimate the quality of approximations of probability distributions towards the construction of small probability spaces. These are used to derandomize algorithms. In contrast to results by Even, Goldreich, Luby, Nisan and Velickovich [EGLNV], our methods are simple, and for reasonably small p, we get smaller sample spaces. Our considerations are motivated by a problem which was mentioned in recent work of Azar, Motwani and Naor [AMN], namely, how to construct in time polynomial in n a good approximation to the joint probability distribution of the random variables X1;X2; : : :;Xn where each Xi has values in f0; 1g and satises Xi = 0 with probability q and Xi = 1 with probability