International Journal of Modern Physics C

We describe and implement a multi-scale acceleration algorithm for spin models on a massively parallel supercomputer, the Connection Machine CM-200. Unlike usual cluster algorithms, our algorithm is completely parallelizable. The time to update all variables in a system of volume L^d scales as L^dlog₂L. We prove this by computing the time for one lattice sweep for the 2-d XY model for our algorithm on lattices of size up to 2048×2048.

Neural Network Classifiers are used to separate the signal from the background in a High-Energy Physics problem. The basic principles of Multidimensional Analysis by means of Back-Propagation Neural Network Classifiers and the application to the Higgs search are discussed. A search for the Minimal Standard Model Higgs boson, through the reaction , using the data collected in 1990 by the DELPHI detector at LEP is made. The technique used allows to reach good detection efficiencies and no evidence of the Minimal Standard Model Higgs boson with mass less than 37 GeV is found. The use of Neural Network Classifiers proves to be a very powerful classification method. A comparison of the method with standard analysis techniques is presented.

We consider the scattering of solitons and antisolitons in a class of models in (2+1) dimensions. We point out that although in general the interaction forces between solitons and antisolitons are attractive, in some σ models they depend on the relative orientation between the solitons and antisolitons in their internal space. We discuss the scattering properties of such solitons and antisolitons.

We introduce the notion of a class of abstract digital computers based on quantum reversibility and indeterminism (not 011 the classical sequential architecture), which are compatible with quantum laws and may perform nondeterministic and non-recursive computation.

Matched Field Processing (MFP) is a signal processing technique that has enjoyed much recent success in the Underwater Acoustics community, mainly as a consequence of the high accuracy currently achievable in predictions of non-isotropic, non-planar ocean acoustic fields. MFP is applied to acoustic fields measured on arrays of hydrophones and has been used primarily to solve the inverse source problem, i.e., to determine the unknown range, depth, and bearing of acoustic sources in a known ocean environment. However, the MFP approach has also been applied to the environmental inverse problem, i.e., to determine the characteristics of an unknown ocean environment. This paper will review the work done in this latter area.

In light of present day data-parallel computers, an appraisal of molecular dynamics simulations of large N-particle systems, isolated or in contact with a heat-bath, is given. Special attention is focused 011 the Connection Machine CM-2. Particularly the cases of long-range potentials and impulsive hard-core interactions are discussed in detail. Data-parallel strategies including data distribution, communications and computation are presented and compared with well-known sequential approaches. The conclusion offered is that the methods described here are easy to design and offer the possibility of reasonably fast implementations for the reliable simulation of macroscopic samples of matter.

During the last years, the possibility to use Artificial Neural Networks in experimental High Energy Physics has been widely studied. In particular, applications to pattern recognition and pattern classification problems have been investigated. The purpose of this article is to review the status of such investigations and the techniques established.