Weak scale right-handed neutrino as pseudo-Goldstone fermion of spontaneously broken $$ \mathrm{U}{(1)}_{L_{\mu }-{L}_{\tau }} $$

Possibility of a Right-Handed (RH) neutrino being a Goldstone fermion of a spontaneously broken global U(1) symmetry in a supersymmetric theory is considered. This fermion obtains mass from the supergravity effects leading to a RH neutrino at the electroweak scale with a mass similar to the gravitino mass. A prototype model realizing this scenario contains just three gauge singlet superfields needed for the type I seesaw mechanism. Masses of the other two neutrinos are determined by the U(1) breaking scale which too can be around the electroweak scale. Light neutrinos obtain their masses in this scenario through (a) mixing with the RH neutrinos (type I seesaw), (b) mixing with neutralinos (R-parity breaking), (c) indirectly through mixing of the RH neutrinos with neutralinos, and (d) radiative corrections. All these contributions are described by the same set of a small number of underlying parameters and provide a very constrained and predictive framework for the neutrino masses which is investigated in detail for various choices of U(1) symmetries. It is found that flavour independent U(1) symmetries cannot describe neutrino masses if the soft supersymmetry breaking terms are flavour universal and one needs to consider flavour dependent symmetries. Considering a particular example of Lμ− Lτ symmetry, it is shown that viable neutrino masses and mixing can be obtained without introducing any flavour violation in the soft sector. The leptonic couplings of Majoron are worked out in the model and shown to be consistent with various laboratory, astrophysical and cosmological constraints. The neutrino data allows sizeable couplings between the RH neutrinos and Higgsinos which can be used to probe the pseudo-Goldstone fermion at colliders through its displaced decay vertex.

KINK FINDING IN THE ALEPH TPC USING NEURAL NETWORKS

The task of finding the decays of charged tracks inside a tracking device is divided into two parts. First a neural net is used to recognize kinks in well-reconstructed tracks. If a kink has been found, a second net determines the radial position of the decay vertex. Both algorithms use feed-forward nets with error back-propagation. Very good performance is obtained in comparison with conventional methods using simulated data from the ALEPH TPC. The behavior of the nets is analyzed by studying the correlations between the inputs and the output.