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Record W2011606526 · doi:10.1086/505781

A Search for Kilogauss Magnetic Fields in White Dwarfs and Hot Subdwarf Stars

2006· article· en· W2011606526 on OpenAlexaff
G. G. Valyavin, S. Bagnulo, S. Fabrika, Andreas Reisenegger, G. A. Wade, Inwoo Han, D. Monin

Bibliographic record

VenueThe Astrophysical Journal · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsDominion Astrophysical ObservatoryRoyal Military College of Canada
Fundersnot available
KeywordsWhite dwarfSubdwarfPhysicsAstrophysicsStarsMagnetic fieldPolarization (electrochemistry)Astronomy

Abstract

fetched live from OpenAlex

We present new results of a survey for weak magnetic fields among DA white dwarfs, including some brighter hot subdwarf stars. We have detected variable circular polarization in the Hα line of the hot subdwarf star Feige 34 (spectroscopic type: sdO). From these data, we estimate that the longitudinal magnetic field of this star varies from -1.1 ± 3.2 to +9.6 ± 2.6 kG, with a mean of about +5 kG and a period longer than 2 hr. In this study, we also confirm the magnetic nature of white dwarf WD 1105-048, found earlier in a study by Aznar Cuadrado and coworkers, and present upper limits of kilogauss longitudinal magnetic fields of the five brightest DA white dwarfs. Our data support the finding of Aznar Cuadrado and coworkers that ~25% of white dwarfs have kilogauss magnetic fields. This frequency also confirms results of early estimates obtained using the magnetic field function of white dwarfs (Fabrika & Valyavin).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.230
Teacher spread0.220 · 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 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

Citations44
Published2006
Admission routes1
Has abstractyes

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