Hybrid and Conventional Mesons in the Flux Tube Model: Numerical Studies and their Phenomenological Implications

We present results from analytical and numerical studies of a flux tube model of hybrid mesons. Our numerical results use a Hamiltonian Monte Carlo algorithm and so improve on previous analytical treatments, which assumed small flux tube oscillations and an adiabatic separation of quark and flux tube motion. We find that the small oscillation approximation is inappropriate for typical hadrons and that the hybrid mass is underestimated by the adiabatic approximation. For physical parameters in the ``one-bead" flux tube model we estimate the lightest hybrid masses (${}_\Lambda L = {}_1 P$ states) to be 1.8-1.9~GeV for $u\bar u$ hybrids, 2.1-2.2~GeV for $s\bar s$ and 4.1-4.2~GeV for $c\bar c$. We also determine masses of conventional $q\bar q$ mesons with $L=0$ to $L=3$ in this model, and confirm good agreement with experimental $J$-averaged multiplet masses. Mass estimates are also given for hybrids with higher orbital and flux-tube excitations. The gap from the lightest hybrid level (${}_1P$) to the first hybrid orbital excitation (${}_1D$) is predicted to be $\approx 0.4$~GeV for light quarks $(q=u,d)$ and $\approx 0.3$~GeV for $q=c$. Both ${}_1P$ and ${}_1D$ hybrid multiplets contain the exotics $1^{-+}$ and $2^{+-}$; in addition the ${}_1P$ has a $0^{+-}$ and the ${}_1D$ contains a $3^{-+}$. Hybrid mesons with doubly-excited flux tubes are also considered. The implications of our results for spectroscopy are discussed, with emphasis on charmonium hybrids, which may be accessible at facilities such as BEPC, KEK, a Tau-Charm Factory, and in $\psi$ production at hadron colliders.Comment: 39 pages of RevTex. Figures available via anonymous ftp at ftp://compsci.cas.vanderbilt.edu/QSM/bcsfig1.ps and /QSM/bcsfig6.p

A Prototype Neural-network To Perform Early Warning in Nuclear-power-plant

The paper presents some results of research work in the field of artificial neural networks (ANN) applied to nuclear safety. It shows how a priori knowledge in the form of qualitative physical reasoning can provide a powerful basis for designing a set of ANN-based detection subsystems. In particular, it explains how each ANN is in charge of modelling a physical relationship between a set of state variables (thermal balance, mass balance, etc.) by trying to predict one particular variable from other ones; then, the residual signal, defined by the difference between the predicted value and the real one is used to decide whether abnormalities are present. As far as the decision logic is concerned, the paper describes how robustness can be improved by adequate filters on the residuals. The proposed approach is then validated on data coming from a fullscope simulator of one of the Belgian nuclear power units: the neural-based detection system is trained on ''normal'' scenarios and is able, after learning, to detect reliably and rapidly most of the incidental situations chosen as tests