24 research outputs found
A Compromise between Neutrino Masses and Collider Signatures in the Type-II Seesaw Model
A natural extension of the standard gauge
model to accommodate massive neutrinos is to introduce one Higgs triplet and
three right-handed Majorana neutrinos, leading to a neutrino mass
matrix which contains three sub-matrices ,
and . We show that three light Majorana neutrinos (i.e., the mass
eigenstates of , and ) are exactly massless in this
model, if and only if
exactly holds. This no-go theorem implies that small but non-vanishing neutrino
masses may result from a significant but incomplete cancellation between
and terms in the Type-II
seesaw formula, provided three right-handed Majorana neutrinos are of TeV and experimentally detectable at the LHC. We propose three simple
Type-II seesaw scenarios with the flavor symmetry to
interpret the observed neutrino mass spectrum and neutrino mixing pattern. Such
a TeV-scale neutrino model can be tested in two complementary ways: (1)
searching for possible collider signatures of lepton number violation induced
by the right-handed Majorana neutrinos and doubly-charged Higgs particles; and
(2) searching for possible consequences of unitarity violation of the neutrino mixing matrix in the future long-baseline neutrino oscillation
experiments.Comment: RevTeX 19 pages, no figure
The time dimension of neural network models
This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented
Optimal Design in Flexible Models, Including Feed-Forward Networks and Nonparametric Regression
No abstract available