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Record W3083466224 · doi:10.1007/jhep02(2021)010

Stealth decaying spin-1 dark matter

2021· article· en· W3083466224 on OpenAlexfundno aff
Cédric Delaunay, T. Ma, Yotam Soreq

Bibliographic record

VenueJournal of High Energy Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationUnited States-Israel Binational Science FoundationNational Science Foundation
KeywordsPhysicsDark matterParticle physicsHyperchargeNeutrinoScalar field dark matterStandard Model (mathematical formulation)LeptonPhysics beyond the Standard ModelLight dark matterPhenomenology (philosophy)Dark fluidHot dark matterHiggs bosonCosmologyAstrophysicsElectronNuclear physicsDark energyGauge (firearms)

Abstract

fetched live from OpenAlex

Abstract We consider models of decaying spin-1 dark matter whose dominant coupling to the standard model sector is through a dark-Higgs Yukawa portal connecting a TeV-scale vector-like lepton to the standard model (right-handed) electron. Below the electron-positron threshold, dark matter has very slow, loop-suppressed decays to photons and (electron) neutrinos, and is stable on cosmological time-scale for sufficiently small gauge coupling values. Its relic abundance is set by in-equilibrium dark lepton decays, through the freeze-in mechanism. We show that this model accommodates the observed dark matter abundance for natural values of its parameters and a dark matter mass in the ∼ 5 keV to 1 MeV range, while evading constraints from direct detection, indirect detection, stellar cooling and cosmology. We also consider the possibility of a nonzero gauge kinetic mixing with the standard model hypercharge field, which is found to yield a mild impact on the model’s phenomenology.

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 score0.005
Threshold uncertainty score0.017

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 studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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