Deep learning analysis of the inverse seesaw in a 3-3-1 model at the LHC

D Cogollo,F.F Freitas,C.A De S Pires,Yohan M Oviedo-Torres,P Vasconcelos

doi:10.1016/j.physletb.2020.135931

Abstract

Inverse seesaw is a genuine TeV scale seesaw mechanism. In it active neutrinos with masses at eV scale requires lepton number be explicitly violated at keV scale and the existence of new physics, in the form of heavy neutrinos, at TeV scale. Therefore it is a phenomenologically viable seesaw mechanism since its signature may be probed at the LHC. Moreover it is successfully embedded into gauge extensions of the standard model as the 3-3-1 model with the right-handed neutrinos. In this work we revisit the implementation of this mechanism into the 3-3-1 model and employ deep learning analysis to probe such setting at the LHC and, as main result, we have that if its signature is not detected in the next LHC running with energy of 14 TeVs, then, the vector boson Z′ of the 3-3-1 model must be heavier than 4 TeVs.

Highlights

Seesaw mechanisms [1,2,3,4] are seem as the simplest proposals to solve the long-standing problem of the smallness of the neutrino masses
Researchers have focused their investigations on phenomenologically viable seesaw mechanisms, as inverse seesaw one [4], since their signatures may be probed at the LHC [5]
The distinguishable aspect of the inverse seesaw (ISS) mechanism is the fact that it is a genuine TeV scale seesaw mechanism and according to the original idea [4] its implementation requires the addition of six new neutrinos (NiR, SiL with i = 1, 2, 3) to the standard model particle content composing the following bilinear terms [6], L

Summary

Introduction

Seesaw mechanisms [1,2,3,4] are seem as the simplest proposals to solve the long-standing problem of the smallness of the neutrino masses. The double suppression by the mass scale connected with M turns it possible to have such scale much below than that one involved in the canonical seesaw mechanism [1,2,3] It happens that standard neutrinos with mass at sub-eV scale are obtained for mD at electroweak scale, M at TeV scale and μ at keV scale. The proposal of this work is to complete this job and probe the ISS in 331RHN at the LHC For this purpose we review the model, the mechanism, and employ deep learning to probe the signature of the mechanism at the LHC by means of the production of these new neutrinos and their detection in the form of leptons as final products.

Some essential points of the model and of the mechanism

Analysis of the production mechanism and main channels

Z channel

Deep learning analysis: methods and results

Findings

Conclusions