Detection of Charged MSSM Higgs Bosons at CERN LEP-II and NLC

We study the possibility of detecting the charged Higgs bosons predicted in the Minimal Supersymmetric Standard Model $(H^\pm)$, with the reactions $e^{+}e^{-}\to \tau^-\bar \nu_{\tau}H^+, \tau^+\nu_\tau H^-$, using the helicity formalism. We analyze the region of parameter space $(m_{A^0}-\tan\beta)$ where $H^\pm$ could be detected in the limit when $\tan\beta$ is large. The numerical computation is done for the energie which is expected to be available at LEP-II ($\sqrt{s}=200$ GeV) and for a possible Next Linear $e^{+}e^{-}$ Collider ($\sqrt{s}=500$ GeV).Comment: Latex file and 5 figure

Bounds on the dipole moments of the tau-neutrino via the process e+e−→ννˉγe^{+}e^{-}\rightarrow \nu \bar \nu \gamma in a 331 model

We obtain limits on the anomalous magnetic and electric dipole moments of the $\nu_{\tau}$ through the reaction $e^{+}e^{-}\rightarrow \nu \bar \nu \gamma$ and in the framework of a 331 model. We consider initial-state radiation, and neglect $W$ and photon exchange diagrams. The results are based on the data reported by the L3 Collaboration at LEP, and compare favorably with the limits obtained in other models, complementing previous studies on the dipole moments.Comment: 13 pages, 4 figures, to be published in The European Physical J C. arXiv admin note: substantial text overlap with arXiv:hep-ph/060527

Logic Negation with Spiking Neural P Systems

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur