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Record W2056425191 · doi:10.1088/2041-8205/739/2/l47

GALAXY DISKS ARE SUBMAXIMAL

2011· article· en· W2056425191 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsGalaxySpiral galaxyLuminosityGalaxy rotation curveVelocity dispersionStarsTully–Fisher relationBaryonStellar massRotation (mathematics)

Abstract

fetched live from OpenAlex

We measure the contribution of galaxy disks to the overall gravitational potential of 30 nearly face-on intermediate-to-late-type spirals from the DiskMass Survey. The central vertical velocity dispersion of the disk stars (σ disk z , R = 0 ) is related to the maximum rotation speed ( V max ) as σ disk z , R = 0 ∼ 0.26 V max , consistent with previous measurements for edge-on disk galaxies and a mean stellar velocity ellipsoid axial ratio α ≡ σ z /σ R = 0.6. For reasonable values of disk oblateness, this relation implies these galaxy disks are submaximal. We find disks in our sample contribute only 15%–30% of the dynamical mass within 2.2 disk scale lengths ( h R ), with percentages increasing systematically with luminosity, rotation speed, and redder color. These trends indicate that the mass ratio of disk-to-total matter remains at or below 50% at 2.2 h R even for the most extreme, fast-rotating disks ( V max ⩾ 300 km s −1 ) of the reddest rest frame, face-on color ( B − K ∼ 4 mag), and highest luminosity ( M K < −26.5 mag). Therefore, spiral disks in general should be submaximal. Our results imply that the stellar mass-to-light ratio and hence the accounting of baryons in stars should be lowered by at least a factor of three.

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)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.013
GPT teacher head0.198
Teacher spread0.186 · 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

Citations103
Published2011
Admission routes1
Has abstractyes

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