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.
Keung-Senjanovi\'c process at LHC: from LNV to displaced vertices to invisible decays
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abstract
In the context of Left-Right symmetry, we revisit the Keung-Senjanovi\'{c} production of right-handed $W_R$ bosons and heavy neutrinos $N$ at high energy colliders. We develop a multi-binned sensitivity measure and use it to estimate the sensitivity for the entire range of $N$ masses, spanning the standard and merged prompt signals, displaced vertices and the invisible $N$ region. The estimated sensitivity of the LHC with 300/fb integrated luminosity ranges from 5 to beyond 7 TeV, while the future 33(100) TeV collider's reach with 3/ab extends to 12(26) TeV.
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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.