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Record W2207334608 · doi:10.1007/jhep07(2016)016

Stealth Supersymmetry simplified

2016· article· en· W2207334608 on OpenAlexaff

Bibliographic record

VenueJournal of High Energy Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
FundersOak Ridge Institute for Science and EducationOffice of ScienceAspen Center for PhysicsFAS Division of Science, Harvard UniversityU.S. Department of EnergyHarvard UniversityOak Ridge Associated UniversitiesNational Science Foundation
KeywordsGluinoSupersymmetryHiggsinoLarge Hadron ColliderSupersymmetry breakingSuperpartnerPhysics beyond the Standard Model

Abstract

fetched live from OpenAlex

In Stealth Supersymmetry, bounds on superpartners from direct searches can be notably weaker than in standard supersymmetric scenarios, due to suppressed missing energy. We present a set of simplified models of Stealth Supersymmetry that motivate 13 TeV LHC searches. We focus on simplified models within the Natural Supersymmetry framework, in which the gluino, stop, and Higgsino are assumed to be lighter than other superpartners. Our simplified models exhibit novel decay patterns that differ significantly from topologies of the Minimal Supersymmetric Standard Model, with and without R-parity. We determine limits on stops and gluinos from searches at the 8 TeV LHC. Existing searches constitute a powerful probe of Stealth Supersymmetry gluinos with certain topologies. However, we identify simplified models where the gluino can be considerably lighter than 1 TeV. Stops are significantly less constrained in Stealth Supersymmetry than the MSSM, and we have identified novel stop decay topologies that are completely unconstrained by existing LHC searches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.247
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2016
Admission routes1
Has abstractyes

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