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  • Open Access

Probing lepton flavor mixing in WR searches with machine learning at the LHC

Jin-Man Cai1,* and Gang Li1,2,†

  • *Contact author: caijm7@mail2.sysu.edu.cn
  • †Contact author: ligang65@mail.sysu.edu.cn
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Phys. Rev. D 114, 055037 – Published 21 September, 2026

DOI: https://doi.org/10.1103/fzws-k9rl

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, 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 Pontecorvo-Maki-Nakagawa-Sakata (PMNS)-like. In the unmixed scenario, the deep neural network 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) 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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Phys. Rev. D 114, 055037– Published 21 September, 2026
  • Received 9 June 2026
  • Accepted 26 August 2026
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