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Record W4388183661 · doi:10.48550/arxiv.2310.18397

Detectable and defect-free dark photon dark matter

2023· preprint· en· W4388183661 on OpenAlexfundno aff
David Cyncynates, Zachary J. Weiner

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersMinistry of Colleges and UniversitiesGovernment of CanadaInnovation, Science and Economic Development CanadaInstitut Périmètre de physique théoriqueUniversity of WashingtonAspen Center for Physics
KeywordsPhysicsDark photonDark matterDark fluidPhotonScalar field dark matterAstrophysicsCold dark matterUniverseHot dark matterHidden sectorLight dark matterCosmologyParticle physicsAstronomyDark energyQuantum mechanics

Abstract

fetched live from OpenAlex

Ultralight dark photons are compelling dark matter candidates, but their allowed kinetic mixing with the Standard Model photon is severely constrained by requiring that the dark photons do not collapse into a cosmic string network in the early Universe. Direct detection in minimal production scenarios for dark photon dark matter is strongly limited, if not entirely excluded; discovery of sub-meV dark photon dark matter would therefore point to a nonminimal dark sector. We describe a model that evades such constraints, capable of producing cold dark photons in any parameter space accessible to future direct detection experiments. The associated production dynamics yield additional signatures in cosmology and small-scale structure, allowing for possible positive identification of this particular class of production mechanisms.

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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.173
Teacher spread0.140 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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