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Record W2244059522 · doi:10.1093/mnrasl/slv130

First discovery of a magnetic field in a main-sequence δ Scuti star: the <i>Kepler</i> star HD 188774

2015· article· en· W2244059522 on OpenAlexaboutno aff
C. Neiner, P. Lampens

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsAstrophysicsKeplerPlanetStarspotLight curveStar (game theory)AsteroseismologyMagnetic fieldAstronomyStars

Abstract

fetched live from OpenAlex

Abstract The Kepler space mission provided a wealth of δ Sct–γ Dor hybrid candidates. While some may be genuine hybrids, others might be misclassified due to the presence of a binary companion or to rotational modulation caused by magnetism and related surface inhomogeneities. In particular, the Kepler δ Sct–γ Dor hybrid candidate HD 188774 shows a few low frequencies in its light and radial velocity curves, whose origin is unclear. In this work, we check for the presence of a magnetic field in HD 188774. We obtained two spectropolarimetric measurements with an Echelle SpectroPolarimetric Device for the Observation of Stars (ESPaDOnS) at Canada–France–Hawaii Telescope. The data were analysed with the least-squares deconvolution (LSD) method. We detected a clear magnetic signature in the Stokes V LSD profiles. The origin of the low frequencies detected in HD 188774 is therefore most probably the rotational modulation of surface spots possibly related to the presence of a magnetic field. Consequently, HD 188774 is not a genuine hybrid δ Sct–γ Dor star, but the first known magnetic main-sequence δ Sct star. This makes it a prime target for future asteroseismic and spot modelling. This result casts new light on the interpretation of the Kepler results for other δ Sct–γ Dor hybrid candidates.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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

Citations39
Published2015
Admission routes1
Has abstractyes

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