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We analyze the scenario within the Next to Minimal Supersymmetric Standard Model (NMSSM), where the lightest supersymmetric particle (LSP) is singlino-like neutralino. By systematically considering various possible admixtures in the electroweakino sector, we classify regions of parameter space where the next to lightest supersymmetric particle (NLSP) is a long-lived electroweakino while remaining consistent with constraints from flavor physics, dark matter direct detection, and collider data. We identify viable cascade decay modes featuring the long-lived NLSP for directly produced chargino-neutralino pairs, thus, leading to displaced vertex signatures at the high luminosity LHC (HL-LHC). We construct track based analysis in order to uncover such scenarios at the HL-LHC and analyze their discovery potential. We show that through such focused searches for the long-lived particles at the HL-LHC, one can probe regions of the electroweakino parameter space that are otherwise challenging.
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Shedding Light on Dark Matter at the LHC with Machine Learning
A machine-learned LHC analysis projects 5-sigma sensitivity to singlino-dominated NMSSM dark matter via radiative higgsino decays to photons, covering higgsino masses up to 225 GeV.
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