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Record W2883908489 · doi:10.1515/astro-2018-0012

Rotation in sdB stars as revealed by stellar oscillations

2018· article· en· W2883908489 on OpenAlexaff
S. Charpinet, N. Giammichele, Weikai Zong, V. Van Grootel, P. Brassard, G. Fontaine

Bibliographic record

VenueOpen Astronomy · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsPhysicsAsteroseismologyAstrophysicsStarsAstronomyRed giantSubdwarfRed-giant branchWhite dwarfStellar rotationHorizontal branchRotation (mathematics)Stellar evolutionStellar collisionK-type main-sequence starT Tauri starMetallicity

Abstract

fetched live from OpenAlex

Abstract An interesting opportunity offered by the detection of stellar oscillations is the possibility to infer the internal rotation rate of a star through the so-called rotational splittings. Such seismic measurements remained rather scarce for hot B subdwarf (sdB) stars until the advent of space observations with the Kepler spacecraft. Nowadays, however, a number of rotation measurements have become available, offering a glimpse on the global rotational properties of sdB stars. Here, we briefly discuss what asteroseismology starts to reveal on the rotation rate of these stars. We also make connections with the internal rotation of red-giant and white-dwarf stars. In particular, we show that the very slow rotation rates derived for single sdB stars, and their similarities with the dynamical properties of the cores of red-clump stars, strongly suggest that they evolved from red-giants rather than merger events.We also point out that no more angular momentum seems to be lost by stellar cores throughout the helium burning phase until the cooling white-dwarf stage, indicating that all the braking occurs before, most likely during red-giant branch evolution.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

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