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Discovery of an extremely weak magnetic field in the white dwarf LTT 16093 = WD 2047+372

2016· article· en· W3104197050 on OpenAlexaffabout
J. D. Landstreet, S. Bagnulo, A. J. Martin, G. G. Valyavin

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsWestern University
Fundersnot available
KeywordsWhite dwarfContext (archaeology)Magnetic fieldPhysicsField (mathematics)White (mutation)AstrophysicsBiologyMathematicsQuantum mechanicsGeneticsPure mathematicsPaleontology

Abstract

fetched live from OpenAlex

Context. Magnetic fields have been detected in several hundred white dwarfs, with strengths ranging from a few kG to several hundred MG. Only a few of the known fields have a mean magnetic field modulus below about 1 MG. Aims. We are searching for new examples of magnetic white dwarfs with very weak fields, and trying to model the few known examples. Our search is intended to be sensitive enough to detect fields at the few kG level. Methods. We have been surveying bright white dwarfs for very weak fields using spectropolarimeters at the Canada-France-Hawaii telescope, the William Herschel Telescope (WHT), the European Southern Observatory, and the Russian Special Astrophysical Observatory. We discuss in some detail tests of the WHT spectropolarimeter ISIS using the known magnetic strong-field Ap star HD 215441 (Babcock’s star) and the long-period Ap star HD 201601 (γ Equ). Results. We report the discovery of a field with a mean field modulus of about 57 kG in the white dwarf LTT 16093 = WD 2047+372. The field is clearly detected through the Zeeman splitting of Hα seen in two separate circularly polarised spectra from two different spectropolarimeters. Zeeman circular polarisation is also detected, but only barely above the 3σ level. Conclusions. The discovery of this field is significant because it is the third weakest field ever unambiguously discovered in a white dwarf, while still being large enough that we should be able to model the field structure in some detail with future observations.

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 score0.089
Threshold uncertainty score0.586

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 science0.0010.000
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

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