Scattering in one dimension: The coupled Schroedinger equation, threshold behaviour and Levinson's theorem

We formulate scattering in one dimension due to the coupled Schr\"{o}dinger equation in terms of the $S$ matrix, the unitarity of which leads to constraints on the scattering amplitudes. Levinson's theorem is seen to have the form $\eta(0) = \pi (n_b + 1/2 n - 1/2 N)$, where $\eta(0)$ is the phase of the $S$ matrix at zero energy, $n_b$ the number of bound states with nonzero binding energy, $n$ the number of half-bound states, and $N$ the number of coupled equations. In view of the effects due to the half-bound states, the threshold behaviour of the scattering amplitudes is investigated in general, and is also illustrated by means of particular potential models.Comment: to appear in Journal of Mathematic Physics, RevTex, 16 pages, 3 figures (PostScript

Scenarios and enhanced, strategies, Case study The Hague Region, the Netherlands

In the PLUREL Analysis report on The Hague Region (Aalbers et al 2009), the region is described with respect to history, landuse, planning context, actors and their strategies regarding developments in the urban fringe. Three strategies are described in more depth. In the current phase of the research, these strategies are assessed with respect to their performance in governance

Factor PD-Clustering

Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed data optimizing a common criterion. Factorial PD-clustering is based on Probabilistic Distance clustering (PD-clustering). PD-clustering is an iterative, distribution free, probabilistic, clustering method. Factor PD-clustering make a linear transformation of original variables into a reduced number of orthogonal ones using a common criterion with PD-Clustering. It is demonstrated that Tucker 3 decomposition allows to obtain this transformation. Factor PD-clustering makes alternatively a Tucker 3 decomposition and a PD-clustering on transformed data until convergence. This method could significantly improve the algorithm performance and allows to work with large dataset, to improve the stability and the robustness of the method

Flavour-conserving oscillations of Dirac-Majorana neutrinos

We analyze both chirality-changing and chirality-preserving transitions of Dirac-Majorana neutrinos. In vacuum, the first ones are suppressed with respect to the others due to helicity conservation and the interactions with a (``normal'') medium practically does not affect the expressions of the probabilities for these transitions, even if the amplitudes of oscillations slightly change. For usual situations involving relativistic neutrinos we find no resonant enhancement for all flavour-conserving transitions. However, for very light neutrinos propagating in superdense media, the pattern of oscillations $\nu_L \to \nu^C_L$ is dramatically altered with respect to the vacuum case, the transition probability practically vanishing. An application of this result is envisaged.Comment: 14 pages, latex 2E, no figure

Coherence of neutrino flavor mixing in quantum field theory

In the simplistic quantum mechanical picture of flavor mixing, conditions on the maximum size and minimum coherence time of the source and detector regions for the observation of interference---as well as the very viability of the approach---can only be argued in an ad hoc way from principles external to the formalism itself. To examine these conditions in a more fundamental way, the quantum field theoretical $S$-matrix approach is employed in this paper, without the unrealistic assumption of microscopic stationarity. The fully normalized, time-dependent neutrino flavor mixing event rates presented here automatically reveal the coherence conditions in a natural, self-contained, and physically unambiguous way, while quantitatively describing the transition to their failure.Comment: 12 pages, submitted to Phys. Rev.

Three heavy jet events at hadron colliders as a sensitive probe of the Higgs sector

Assuming that a non-standard neutral Higgs with an enhanced Yukawa coupling to a bottom quark is observed at future hadron experiments, we propose a method for a better understanding of the Higgs sector. Our procedure is based on "counting" the number of events with heavy jets (where "heavy" stands for a c or b jet) versus b jets, in the final state of processes in which the Higgs is produced in association with a single high p_T c or b jet. We show that an observed signal of the type proposed, at either the Tevatron or the LHC, will rule out the popular two Higgs doublet model of type II as well as its supersymmetric version - the Minimal Supersymmetric Standard Model (MSSM), and may provide new evidence in favor of some more exotic multi Higgs scenarios. As an example, we show that in a version of a two Higgs doublet model which naturally accounts for the large mass of the top quark, our signal can be easily detected at the LHC within that framework. We also find that such a signal may be observable at the upgraded Tevatron RunIII, if the neutral Higgs in this model has a mass around 100 GeV and \tan\beta > 50 and if the efficiency for distinguishing a c jet from a light jet will reach the level of 50%.Comment: Revtex, 11 pages, 4 figures embedded in the text. Main changes with respect to Version 1: Numerical results re-calculated using the CTEQ5L pdf, improved discussion on the experimental consequences, new references added. Conclusions remain unchanged. As will appear in Phys. Rev.

Algorithms and literate programs for weighted low-rank approximation with missing data

Linear models identification from data with missing values is posed as a weighted low-rank approximation problem with weights related to the missing values equal to zero. Alternating projections and variable projections methods for solving the resulting problem are outlined and implemented in a literate programming style, using Matlab/Octave's scripting language. The methods are evaluated on synthetic data and real data from the MovieLens data sets