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Record W2061394296 · doi:10.1007/jhep06(2014)083

New limits on light hidden sectors from fixed-target experiments

2014· article· en· W2061394296 on OpenAlexaff
David E. Morrissey, Andrew Spray

Bibliographic record

VenueJournal of High Energy Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsTRIUMF
Fundersnot available
KeywordsHidden sectorHiggs bosonPhysicsHyperchargeMultipletParticle physicsMuonPhysics beyond the Standard ModelStandard Model (mathematical formulation)Abelian groupTheoretical physicsVector bosonSimple (philosophy)Quantum mechanicsMathematicsPure mathematicsSpectral line

Abstract

fetched live from OpenAlex

New physics can be light if it is hidden, coupling very weakly to the Standard Model. In this work we investigate the discovery prospects of Abelian hidden sectors in lower-energy fixed-target and high-precision experiments. We focus on a minimal supersymmetric realization consisting of an Abelian vector multiplet, coupled to hypercharge by kinetic mixing, and a pair of chiral Higgs multiplets. This simple theory can give rise to a broad range of experimental signals, including both commonly-studied patterns of hidden vector decay as well as new and distinctive hidden sector cascades. We find limits from the production of hidden states other than the vector itself. In particular, we show that if the hidden Abelian symmetry is higgsed, and the corresponding hidden Higgs boson has visible decays, it severely restricts the ability of the hidden sector to explain the anomalous muon magnetic moment.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.006
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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 designObservational
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

Citations49
Published2014
Admission routes1
Has abstractyes

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