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Record W1621497258 · doi:10.48550/arxiv.hep-ph/0102158

Primordial Galactic Magnetic Fields: An Application of QCD Domain Walls

2001· preprint· en· W1621497258 on OpenAlexaff
Michael McNeil Forbes, Ariel Zhitnitsky

Bibliographic record

VenueArXiv.org · 2001
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsQuantum chromodynamicsDomain (mathematical analysis)Magnetic fieldAstronomyAstrophysicsParticle physicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

We present a mechanism for generating primordial magnetic fields with large correlation lengths on the order of 100 kpc today. The mechanism is based on recently conjectured QCD domain walls or similar CP violating domain walls with QCD scale structure. Such domain walls align the electric and magnetic dipole moments of the nucleons shortly after the QCD phase transition, producing electromagnetic fields correlated along the domain walls. Through the Kibble mechanism, the domain walls attain Hubble-scale correlations which they transfer to the aligned electromagnetic fields. Due to the CP violation, the Hubble-scale walls produce helical (non-zero Chern-Simons) magnetohydrodynamic turbulence which then undergoes an inverse cascade, allowing the correlation lengths to grow to 100 kpc today. We present an estimate the magnitude of the generated electromagnetic fields in terms of the QCD parameters. We also discuss some other unexplained astrophysical phenomena which may be related to this mechanism. In particular, we discuss the relation between primordial magnetic fields and the Greisen-Zatsepin-Kuzmin (GZK) cutoff violations. We also outline how, by creating inhomogeneities in the nucleon density, QCD domain walls may lead to inhomogeneous big bang nucleosynthesis (IBBN) explaining the Omega_B excess recently measured through cosmic microwave background (CMB) distortions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.335
Teacher spread0.312 · 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 teacher head, 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

Citations1
Published2001
Admission routes1
Has abstractyes

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