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Record W4306409833 · doi:10.1103/physrevd.106.083514

Self-interacting dark baryons

2022· article· en· W4306409833 on OpenAlexafffund
James M. Cline, Cédrick Perron

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsParticle physicsDark matterPionDark photonNucleonQuantum chromodynamicsHidden sectorScalar field dark matterBaryonDark energyAstrophysicsCosmology

Abstract

fetched live from OpenAlex

Using results from lattice QCD, it is possible to quantitatively design models of dark baryons leading to velocity-dependent self-interaction cross sections that match the values needed for solving small-scale structure problems of standard cold dark matter. However it is not obvious that the main dark matter component in such models will be nucleons rather than large nuclei, or dark pions or atoms, whose scattering properties would be different. We first identify the parameters of a dark SU(3) sector analogous to QCD---the confinement scale $\mathrm{\ensuremath{\Lambda}}$ and pion mass ${m}_{\ensuremath{\pi}}$---needed to reproduce desired self-interaction cross sections. Then we show that these values can generically be compatible with the absence of a sufficiently stable deuteron bound state, and hence leading to no heavier nuclei, thus establishing the consistency of the scenario for self-interacting dark nucleons. The range of dark photon masses needed to avoid dominant pion or atomic dark matter is determined, as well as allowed values for the kinetic mixing parameter. The dark proton might be detected directly in future searches, by dark photon exchange.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.381
Teacher spread0.370 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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