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A Stealth Supersymmetry Sampler

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arxiv 1201.4875 v1 pith:L7K33PCH submitted 2012-01-23 hep-ph

classification hep-ph
keywords stealthsupersymmetrymodelsbreakingenergygravitinomissingparticle
verification ladder T0 review T1 audit T2 compute T3 formal
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The LHC has strongly constrained models of supersymmetry with traditional missing energy signatures. We present a variety of models that realize the concept of Stealth Supersymmetry, i.e. models with R-parity in which one or more nearly-supersymmetric particles (a "stealth sector") lead to collider signatures with only a small amount of missing energy. The simplest realization involves low-scale supersymmetry breaking, with an R-odd particle decaying to its superpartner and a soft gravitino. We clarify the stealth mechanism and its differences from compressed supersymmetry and explain the requirements for stealth models with high-scale supersymmetry breaking, in which the soft invisible particle is not a gravitino. We also discuss new and distinctive classes of stealth models that couple through a baryon portal or Z' gauge interactions. Finally, we present updated limits on stealth supersymmetry in light of current LHC searches.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Search for massive, long-lived particles in events with displaced vertices and displaced muons in $pp$ collisions at $\sqrt{s}=13.6$ TeV with the ATLAS experiment

    hep-ex 2026-03 conditional novelty 6.0 of 10

    No signal of displaced-vertex-plus-displaced-muon events is seen in 164 fb^-1 of 13.6 TeV pp data; ATLAS sets improved 95% CL limits on RPV SUSY higgsino and stop production.

  2. Search for long-lived particles using displaced vertices with low-momentum tracks in proton-proton collisions at $\sqrt{s}$ = 13 TeV

    hep-ex 2025-11 conditional novelty 6.0 of 10

    A displaced-vertex search for compressed-spectrum long-lived particles excludes top squarks up to about 1100 GeV and wino-like neutralinos up to about 550 GeV, the strongest limits on these benchmark models.

  3. Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

    hep-ex 2025-06 conditional novelty 6.0 of 10

    The new nonclosure loss term, made differentiable with a sigmoid approximation, lets the neural network optimize the ABCD background estimate directly, improving closure and training stability.

  4. Shedding Light on Dark Matter at the LHC with Machine Learning

    hep-ph 2025-09 conditional novelty 5.0 of 10

    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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