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Record W4391631636 · doi:10.3847/2041-8213/ad3100

A Short Intense Dynamo at the Onset of Crystallization in White Dwarfs

2024· preprint· en· W4391631636 on OpenAlexafffund
J. R. Fuentes, Matias Castro-Tapia, A. Cumming

Bibliographic record

VenueThe Astrophysical Journal Letters · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsWhite dwarfDynamoConvectionPhysicsAstrophysicsMagnetic fieldDynamo theoryRotation (mathematics)CrystallizationIntensity (physics)StarsMechanicsGeometryThermodynamicsOptics

Abstract

fetched live from OpenAlex

Abstract The origin of large magnetic fields (≳10 6 G) in isolated white dwarfs is not clear. One possible explanation is that crystallization of the star’s core drives compositional convection, which when combined with the star’s rotation, can drive a dynamo. However, whether convection is efficient enough to explain the large intensity of the observed magnetic fields is still under debate. Recent work has shown that convection in cooling white dwarfs spans two regimes: efficient convection at the onset of crystallization, and thermohaline convection during most of the star’s cooling history. Here, we calculate the properties of crystallization-driven convection for cooling models of several white dwarfs of different masses. We combine mixing-length theory with scalings from magnetorotational convection to estimate the typical magnitude of the convective velocity and induced magnetic field for both scenarios. In the thermohaline regime, we find velocities ∼10 −6 –10 −5 cm s −1 , with fields restricted to ≲ 100 G. However, when convection is efficient, the flow velocity can reach magnitudes of ∼10 2 –10 3 cm s −1 , with fields of ∼10 6 –10 8 G, independent of the star’s rotation rate. Thus, dynamos driven at the onset of crystallization could explain the large intensity magnetic fields measured for single white dwarfs.

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 score0.806
Threshold uncertainty score0.795

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 science0.0010.001
Research integrity0.0000.002
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 head0.009
GPT teacher head0.222
Teacher spread0.214 · 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

Citations20
Published2024
Admission routes2
Has abstractyes

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