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Record W2019441349 · doi:10.1007/jhep08(2013)008

Strong signatures of right-handed compositeness

2013· article· en· W2019441349 on OpenAlexaff

Bibliographic record

VenueJournal of High Energy Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsYork University
FundersScience and Technology Facilities CouncilMinistero dell’Istruzione, dell’Università e della RicercaDeutsche Forschungsgemeinschaft
KeywordsLarge Hadron ColliderParameter spacePhysics beyond the Standard ModelQuarkStandard Model (mathematical formulation)Signature (topology)Sensitivity (control systems)Atlas (anatomy)Top quark

Abstract

fetched live from OpenAlex

Abstract Right-handed light quarks could be significantly composite, yet compatible with experimental searches at the LHC and precision tests on Standard Model couplings. In these scenarios, that are motivated by flavor physics, one expects large cross sections for the production of new resonances coupled to light quarks. We study experimental strong signatures of right-handed compositeness at the LHC, and constrain the parameter space of these models with recent results by ATLAS and CMS. We show that the LHC sensitivity could be significantly improved if dedicated searches were performed, in particular in multijet signals.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations48
Published2013
Admission routes1
Has abstractyes

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