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Record W4412750267 · doi:10.1093/mnras/staf1254

Simulating the Milky Way bar and bulge with an initially Sérsic disc

2025· article· en· W4412750267 on OpenAlexafffund
Nathan Deg, Victor P. Debattista, Lawrence M. Widrow, Stuart Robert Anderson, O. A. González, Thomas Quinn

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsQueen's University
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaDepartment for Business, Innovation and Skills
KeywordsPhysicsBulgeMilky WayBar (unit)AstrophysicsAstronomyGalaxy

Abstract

fetched live from OpenAlex

Abstract We model the formation of a bar plus box/peanut bulge (BP bulge) component in a Milky Way-like disc galaxy using simulations of isolated multi-component systems that evolve from equilibrium initial conditions. The simulations are designed to test the hypothesis that the bar forms early on and thickens to create the bulge. To this end, our initial conditions include a stellar disc with a Sérsic surface density profile and do not include any classical bulge component. We also include a gas disc, which is important in regulating the growth of the bar. Our best-fit model has an initial stellar disc with a Sersic index of n = 1.75 and a gas disc with mass equal to 7% of the mass of the stellar disc. The model reproduces the bar size, pattern speed, and box/peanut shape of the Milky Way’s bulge+bar.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.200
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 designSimulation or modeling
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 routes2
Has abstractyes

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