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

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.

References in corpus (6)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Right-Handed Neutrinos at LHC and the Mechanism of Neutrino Mass Generation
• Non-Standard Neutrino Interactions with Matter from Physics Beyond the Standard Model
• Lepton Flavour Violating Decays tau to lll and mu to e gamma in the Higgs Triplet Model
• LHC Phenomenology of Type II Seesaw: Nondegenerate Case
• Triple seesaw mechanism

Cited by in corpus (4)

• Phenomenology of vector-like leptons with Deep Learning at the Large Hadron Collider
• A model for mixed warm and hot right-handed neutrino dark matter
• Role of polarizations and spin-spin correlations of $W^{\prime }s$ in $e^{-}{e}^{+}\to W^{-}{W}^{+}$ at $\sqrt{s}=250$ GeV to probe anomalous $W^{-}{W}^{+}Z/\gamma$ couplings
• Fast multilabel classification of HEP constraints with deep learning