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Data from LHC Run-I and Run-II have placed strong constraints on the electroweakino masses in R-parity conserved supersymmetric scenarios. However, in R-parity violating (RPV) scenarios, these constraints can vary depending on the specific RPV decay modes of the lightest supersymmetric particles (LSP). In this work, we explore the potential implications of the baryon-number-violating \(\lambda _{ijk}^{\prime \prime }U_i^cD_j^cD_k^c\) RPV coupling, that allows the LSP to decay into three quarks, on the chargino-neutralino pair production processes at the High Luminosity LHC ( \(\sqrt{s}=14\) TeV, \(\mathcal {L}=3000~\mathrm {fb^{-1}}\) ). We employ machine learning techniques to enhance the search sensitivity.

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Boosting Electroweakino Searches at the HL-LHC in Baryon Number Violating MSSM Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Rahool Barman,
  • Arghya Choudhury,
  • Subhadeep Sarkar

摘要

Data from LHC Run-I and Run-II have placed strong constraints on the electroweakino masses in R-parity conserved supersymmetric scenarios. However, in R-parity violating (RPV) scenarios, these constraints can vary depending on the specific RPV decay modes of the lightest supersymmetric particles (LSP). In this work, we explore the potential implications of the baryon-number-violating \(\lambda _{ijk}^{\prime \prime }U_i^cD_j^cD_k^c\) RPV coupling, that allows the LSP to decay into three quarks, on the chargino-neutralino pair production processes at the High Luminosity LHC ( \(\sqrt{s}=14\) TeV, \(\mathcal {L}=3000~\mathrm {fb^{-1}}\) ). We employ machine learning techniques to enhance the search sensitivity.