Probing lepton flavor mixing in WR searches with machine learning at the LHC
Abstract
Right-handed lepton flavor mixing in the left-right symmetric model directly affects the production and decay of heavy Majorana neutrinos NR, yet its impact on collider searches remains less explored. Using a deep neural network (DNN), we analyze the Keung-Senjanović process pp → WR → ℓαNR → ℓαℓβjj with ℓα,β=e,μ at LHC Run~2 and the HL-LHC, considering both same-sign and opposite-sign dilepton channels. We adopt three benchmark mixing scenarios: unmixed, maximal-mixing, and PMNS-like. In the unmixed scenario, the DNN improves the expected significance over the cut-based analyses performed by ATLAS, leading to stronger expected sensitivities. For the combined ℓℓ analysis, the HL-LHC is projected to reach mWR and mNR values up to $6.7$ ($6.3$)~TeV and $4.4$ ($4.1$)~TeV, respectively, under maximal (PMNS-like) mixing. For representative mass benchmarks, our analysis indicates that LHC Run~2 can test a significant portion of the |Ve1|−−|Vμ1| plane, and the HL-LHC could probe even smaller mixing values, including the corresponding benchmark points for both the maximal and PMNS-like patterns. Finally, we investigate complementarities with low-energy charged lepton flavor violation processes, where future searches can overlap with or exceed the LHC reach.
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BibTeX
@misc{cai2026probing,
title = {{Probing lepton flavor mixing in $W_R$ searches with machine learning at the LHC}},
author = {Jin-Man Cai and Gang Li},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.03809},
eprint = {2606.03809},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.03809},
}
RIS
TY - UNPB
TI - Probing lepton flavor mixing in W_R searches with machine learning at the LHC
AU - Jin-Man Cai
AU - Gang Li
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2606.03809
UR - https://arxiv.org/abs/2606.03809
AB - Right-handed lepton flavor mixing in the left-right symmetric model directly affects the production and decay of heavy Majorana neutrinos N_R, yet its impact on collider searches remains less explored. Using a deep neural network (DNN), we analyze the Keung-Senjanović process pp → W_R → ℓ_αN_R → ℓ_αℓ_βjj with ℓ_α,β=e,μ at LHC Run~2 and the HL-LHC, considering both same-sign and opposite-sign dilepton channels. We adopt three benchmark mixing scenarios: unmixed, maximal-mixing, and PMNS-like. In the unmixed scenario, the DNN improves the expected significance over the cut-based analyses performed by ATLAS, leading to stronger expected sensitivities. For the combined ℓℓ analysis, the HL-LHC is projected to reach m_W_R and m_N_R values up to $6.7$ ($6.3$)~TeV and $4.4$ ($4.1$)~TeV, respectively, under maximal (PMNS-like) mixing. For representative mass benchmarks, our analysis indicates that LHC Run~2 can test a significant portion of the |V_e1|−−|V_μ1| plane, and the HL-LHC could probe even smaller mixing values, including the corresponding benchmark points for both the maximal and PMNS-like patterns. Finally, we investigate complementarities with low-energy charged lepton flavor violation processes, where future searches can overlap with or exceed the LHC reach.
ER -