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Record W4416195862 · doi:10.3847/1538-4357/ae17b3

After 54 Yr of Bar Instability Studies: A Fresh Surprise

2025· article· en· W4416195862 on OpenAlexaff
J. A. Sellwood, Victor P. Debattista, R. G. Carlberg

Bibliographic record

VenueThe Astrophysical Journal · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInstabilityHaloBar (unit)GalaxyPopulationRange (aeronautics)Stability (learning theory)

Abstract

fetched live from OpenAlex

Abstract The well-known bar instability of rotationally supported disk galaxy models has been studied extensively since its first discovery over half a century ago. We were therefore very surprised to find cases of disks embedded in rigid halos, which on the basis of widely cited criteria should be unstable, that appeared to be robustly stable. Here we show that the unstable bar mode in such simulations was being suppressed by changes to the disk caused by other instabilities having higher angular symmetry that were the first to saturate. Although this may seem like a promising solution to the long-standing puzzle presented by the apparent stability of real disk galaxies, we also show that instability is restored in the same models when the rigid halo is replaced by a live population of particles, where the usual stability conditions apply. Our study has been confined to a narrow range of models, and we cannot therefore exclude the possibility that mode interference may be able to prevent bar formation in other models having live halos.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.003

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 head0.309
Teacher spread0.295 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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