Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 43 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 21 tok/s Pro
GPT-5 High 20 tok/s Pro
GPT-4o 95 tok/s Pro
Kimi K2 180 tok/s Pro
GPT OSS 120B 443 tok/s Pro
Claude Sonnet 4.5 32 tok/s Pro
2000 character limit reached

Neutrino masses and mixings with non-zero $θ_{13}$ in Type I+II Seesaw Models (1210.5074v2)

Published 18 Oct 2012 in hep-ph

Abstract: We study the survivability of neutrino mass models with normal as well as inverted hierarchical mass patterns in the presence of both type I and type II seesaw contributions to neutrino mass within the framework of generic left-right symmetric models. At leading order, the Dirac neutrino mass matrix is assumed to be diagonal with either charged lepton (CL) type or up quark (UQ) type structure which gets corrected by non-leading effects giving rise to deviations from tri-bi-maximal (TBM) mixing and hence non-zero value of $\theta_{13}$. Using the standard form of neutrino mass matrix which incorporates such non-leading effects, we parametrize the neutrino mass matrix incorporating both oscillation as well as cosmology data. Also considering extremal values of Majorana CP phases such that the neutrino mass eigenvalues have the structure $(m_1, -m_2, m_3)$ and $(m_1, m_2, m_3)$, we then calculate the predictions for neutrino parameters in the presence of both type I and type II seesaw contributions, taking one of them dominant and the other sub-dominant. We show that these mass models can survive in our framework with certain exceptions.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube