DNN analysis of pp → WR → ℓNR → ℓℓjj at LHC Run 2 and HL-LHC improves exclusion limits on m_WR and m_NR for unmixed, maximal-mixing, and PMNS-like scenarios over cut-based methods and probes the |Ve1|–|Vμ1| plane.
Left-Right Symmetry: from Majorana to Dirac
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abstract
Probing the origin of neutrino mass by disentangling the seesaw mechanism is one of the central issues of particle physics. We address it in the minimal left-right symmetric model and show how the knowledge of light and heavy neutrino masses and mixings suffices to determine their Dirac Yukawa couplings. This in turn allows one to make predictions for a number of high and low energy phenomena, such as decays of heavy neutrinos, neutrinoless double beta decay, electric dipole moments of charged leptons and neutrino transition moments. We also discuss a way of reconstructing the neutrino Dirac Yukawa couplings at colliders such as the LHC.
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Probing lepton flavor mixing in $W_R$ searches with machine learning at the LHC
DNN analysis of pp → WR → ℓNR → ℓℓjj at LHC Run 2 and HL-LHC improves exclusion limits on m_WR and m_NR for unmixed, maximal-mixing, and PMNS-like scenarios over cut-based methods and probes the |Ve1|–|Vμ1| plane.