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

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 $Z^{\prime}$ of the 3-3-1 model must be heavier than 4 TeVs.