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Record W3099672458

Exploring the origin of neutron star magnetic field: magnetic properties of the progenitor OB stars

2012· article· en· W3099672458 on OpenAlexaff
V. Pétit, G. A. Wade, Laurent Drissen, T. Montmerle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsRoyal Military College of CanadaUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsIconCitationComputer sciencePhysicsNeutron starField (mathematics)World Wide WebAstronomyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Abstract. Ferrario & Wickramasinghe (2006) explored the hypothesis that the magnetic fields of neutron stars are of fossil origin. In this context, they predicted the field distribution of the progenitor OB stars, finding that 5 per cent of main sequence massive stars should have fields in excess of 1 kG. We have carried out sensitive ESPaDOnS spectropolarimetric observations to search for direct evidence of such fields in all massive B- and O-type stars in the Orion Nebula Cluster star-forming region. We have detected unambiguous Stokes V Zeeman signatures in spectra of three out of the eight stars observed (38%). Using a new state-of-the-art Bayesian analysis, we infer the presence of strong (kG), organised magnetic fields in their photospheres. For the remaining five stars, we constrain any dipolar fields in the photosphere to be weaker than about 200 G. Statistically, the chance of finding three ∼ 1kG fields in a sample of eight OB stars is quite low (less than 1%) if the predictions of Ferrario & Wickramasinghe are correct. This implies that either the magnetic fields of neutron stars are not of fossil origin, that the flux-evolution model of Ferrario & Wickramasinghe is incomplete, or that the ONC has unusual magnetic properties. We are undertaking a study of other young star clusters, in order to better explore these possibilities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.224

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.218
Teacher spread0.180 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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