Bulk Majorons at Colliders

Lepton number violation may arise via the spontaneous breakdown of a global symmetry. In extra dimensions, spontaneous lepton number violation in the bulk implies the existence of a Goldstone boson, the majoron J^(0), as well as an accompanying tower of Kaluza-Klein (KK) excitations, J^(n). Even if the zero-mode majoron is very weakly interacting, so that detection in low-energy processes is difficult, the sum over the tower of KK modes may partially compensate in processes of relevance at high-energy colliders. Here we consider the inclusive differential and total cross sections for e^- e^- --> W^- W^- J, where J represents a sum over KK modes. We show that allowed parameter choices exist for which this process may be accessible to a TeV-scale electron collider.Comment: 11 pages LaTeX, 3 eps figures (references added

A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms

The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not sufficiently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist profile likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global fit. We compare our method to the MultiNest algorithm, an efficient Bayesian technique, paying particular attention to the best-fit points and implications for particle masses at the LHC and dark matter searches. Our global best-fit point lies in the focus point region. We find many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m_0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a significant influence on the derived confidence regions for parameters and observables, and can dramatically change the entire statistical inference of such scans.Comment: 47 pages, 8 figures; Fig. 8, Table 7 and more discussions added to Sec. 3.4.2 in response to referee's comments; accepted for publication in JHE

Compact Polyelectrolyte Complexes: “Saloplastic” Candidates for Biomaterials

Precipitates of polyelectrolyte complexes were transformed into rugged shapes suitable for bioimplants by ultracentrifugation in the presence of high salt concentration. Salt ions dope the complex, creating a softer material with viscous fluid-like properties. Complexes that were compacted under the centrifugal field (CoPECs) were made from poly(diallyldimethyl ammonium), PDADMA, as polycation, and poly(styrene sulfonate), PSS, or poly(methacrylic acid), PMAA, as polyanion. Dynamic mechanical testing revealed a rubbery plateau at lower frequencies for PSS/PDADMA with moduli that decreased with increasing salt concentration, as internal ion pair cross-links were broken. CoPECs had significantly lower modulii compared to similar polyelectrolyte complexes prepared by the “multilayering ” method. The difference in mechanical properties was ascribed to higher water content (located in micropores) for the former and, more importantly, to their nonstoichiometric polymer composition. The modulus of PMAA/PDADMA CoPECs, under physiological conditions, demonstrated dynamic mechanical properties that were close to those of the nucleus pulposus in an intervertebral disk

Multivariate decision tree design for the classification of multi-jet topologies in e+e−e^{+}e^{-} collisions

The binary decision tree method is used to separate between several multi-jet topologies in e/sup +/e/sup -/ collisions. Instead of the univariate process usually taken, a new design procedure for constructing multivariate decision trees is proposed. The segmentation is obtained by considering some features functions, where linear and nonlinear discriminant functions and a minimal distance method are used. The classification focuses on ALEPH simulated events, with multi-jet topologies. Compared to a standard univariate tree, the multivariate decision trees offer significantly better performance. (30 refs)

Using neural networks with new morphological variables to recognize the number of jets in e+e−e^{+} e^{-} reactions

In this work, we aim to construct a new set of variables, to recognize the number of jets produced in the e/sup +/ e/sup -/ events. These so-called morphological variables usually used in image processing and recognition problems, are comparable to the classical sphericity, aplanarity etc.. The amelioration of the recognition efficiency is obtained thanks to the use of a back-propagation neural network. The survey first done on the generated Lund Monte Carlo events could be reinforced thereafter by taking into account the simulation of the ALEPH detector. The neural network performed on this later kind of events, successfully identifies the 4 classes of events (event with 2, 3, 4 jets or with an isotropic distribution (0 jets)). (33 refs)

Search for the standard model Higgs boson in e+e−e^{+}e^{-} four- jet topology using neural networks and discriminant analysis

We present an attempt to separate between Higgs boson events (e/sup + /e/sup -/ to ZH to qqbb) and other physics processes in the 4-jet channel (e/sup +/e/sup -/ to Z/ gamma , W/sup +/W, ZZ to 4jets), using the discriminant analysis and neural networks methods. Events were produced at LEP2 energies, using the Lund Monte Carlo generator and the Aleph package. The most discriminant variables as the reconstructed jet mass, the jet properties (b-tag, rapidity weighted moments) and other variables are used. (8 refs)