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While all models of Majorana neutrino masses lead to the same dimension five effective operator, which does not conserve lepton number, the dimension six operators induced at low energies conserve lepton number and differ depending on the high energy model of new physics. We derive the low-energy dimension six operators which are characteristic of generic Seesaw models, in which neutrino masses result from the exchange of heavy fields which may be either fermionic singlets, fermionic triplets or scalar triplets. The resulting operators may lead to effects observable in the near future, if the coefficients of the dimension five and six operators are decoupled along a certain pattern, which turns out to be common to all models. The phenomenological consequences are explored as well, including their contributions to $mu to e gamma$ and new bounds on the Yukawa couplings for each model.
We consider a class of models in which the neutrinos acquire Majorana masses through mixing with singlet neutrinos that emerge as composite states of a strongly coupled hidden sector. In this framework, the light neutrinos are partially composite par
Several cosmologically distant astrophysical sources may produce high-energy cosmic neutrinos (E geq 10^6 GeV) of all flavors above the atmospheric neutrino background. We study the effects of vacuum neutrino mixing in three flavor framework on this
We discuss first the flavor mixing of the quarks, using the texture zero mass matrices. Then we study a similar model for the mass matrices of the leptons. We are able to relate the mass eigenvalues of the charged leptons and of the neutrinos to the
Low Energy solar neutrino detection plays a fundamental role in understanding both solar astrophysics and particle physics. After introducing the open questions on both fields, we review here the major results of the last two years and expectations f
We consider a situation where the leading-order neutrino mass matrix is derived by a theoretical ansatz and reproduces the experimental data well, but not completely. Then, the next stage is to try to fully reproduce the data by adding small perturba