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Neutrino phenomenology, muon and electron (g-2) under U(1) gauged symmetries in an extended inverse seesaw model

Authors: Papia Panda;

Neutrino phenomenology, muon and electron (g-2) under U(1) gauged symmetries in an extended inverse seesaw model

Abstract

The presented manuscript is an extension of standard model, where, we have introduced two anomaly free gauge symmetries i.e. U(1)Le-Lτ and U(1)B-L in an inverse seesaw framework. Therefore, we have included three right handed neutrinos NR, three neutral fermions SL and two scalar singlet bosons (Χ1 , Χ2). We are able to get a definite structure for neutrino mass matrix due to the incorporated gauge symmetries. Therefore, our model is able to predict the neutrino oscillation results which are in accordance with the experimental data and mostly supports normal ordering. The outcome comprises of active neutrino mass, mixing angles, mass square differences, CP phase etc. We also discuss neutrinoless double beta decay effective mass parameter <mee> which gives a strong evidence on the Majorana nature of neutrinos. Its value is well below the current experiment bounds of KamLAND-Zen, CUORE etc. Furthermore, as extended symmetries are local, hence, are associated with one Z boson each i.e. (Z1 , Z2 ) which makes our model feasible to explain current results of electron and muon g-2 through neutral current interactions.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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