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The radial velocity dispersion profile of the Galactic halo: constraining the density profile of the dark halo of the Milky Way

2005· article· en· W2007836958 on OpenAlexfundno aff

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekMcMaster UniversityNational Science Foundation
KeywordsMilky WayVelocity dispersionHaloDark matter haloDark matterRadial velocityGlobular clusterStarsGalactic halo

Abstract

fetched live from OpenAlex

We have compiled a new sample of 240 halo objects with accurate distance and radial velocity measurements, including globular clusters, satellite galaxies, field blue horizontal branch (FHB) stars and red giant stars from the Spaghetti survey. The new data lead to a significant increase in the number of known objects for Galactocentric radii beyond 50 kpc, which allows a reliable determination of the radial velocity dispersion profile out to very large distances. The radial velocity dispersion shows an almost constant value of 120 km s−1 out to 30 kpc and then continuously declines down to 50 km s−1 at about 120 kpc. This fall-off puts important constraints on the density profile and total mass of the dark matter halo of the Milky Way. For a constant velocity anisotropy, the isothermal profile is ruled out, while both a dark halo following a truncated flat (TF) model of mass 1.2+1.8-0.5× 1012 M⊙ and a Navarro, Frenk & White (NFW) profile of mass 0.8+1.2-0.2× 1012 M⊙ and c= 18 are consistent with the data. The significant increase in the number of tracers combined with the large extent of the region probed by these has allowed a more precise determination of the Milky Way mass in comparison to previous works. We also show how different assumptions for the velocity anisotropy affect the performance of the mass models.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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

Citations286
Published2005
Admission routes1
Has abstractyes

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