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

TL;DR

This paper explores the inverse seesaw mechanism within a 3-3-1 model framework, using deep learning to analyze LHC data, and concludes that non-detection of signatures implies a heavier $Z'$ boson than 4 TeV.

Contribution

It introduces a deep learning approach to probe the inverse seesaw in a 3-3-1 model at the LHC, providing bounds on the $Z'$ boson mass based on potential non-detection.

Findings

Deep learning analysis constrains $Z'$ mass to be > 4 TeV if no signal is observed.

The inverse seesaw mechanism can be embedded in the 3-3-1 model and tested at the LHC.

Non-detection at 14 TeV LHC sets significant bounds on new physics parameters.

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