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Record W2003792386 · doi:10.1088/0004-637x/748/1/20

DARK MATTER SUB-HALO COUNTS VIA STAR STREAM CROSSINGS

2012· article· en· W2003792386 on OpenAlexaff
R. G. Carlberg

Bibliographic record

VenueThe Astrophysical Journal · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoHerzberg Institute of Astrophysics
Fundersnot available
KeywordsHaloPhysicsAstrophysicsDark matter haloMilky WayDark matterGalactic haloGalaxyAstronomy

Abstract

fetched live from OpenAlex

Dark matter sub-halos create gaps in the stellar streams orbiting in the halos of galaxies. We evaluate the sub-halo stream crossing integral with the guidance of simulations to find that the linear rate of gap creation, , in a typical cold dark matter (CDM) galactic halo at 100 kpc is , where ) is the minimum mass halo that creates a visible gap. The relation can be recast entirely in terms of observables, as , for w in kpc, normalized at 100 kpc. Using published data, the density of gaps is estimated for M31's NW stream and the Milky Way Pal 5 stream, Orphan stream, and Eastern Banded Structure. The estimated rates of gap creation all have errors of 50% or more due to uncertain dynamical ages and the relatively noisy stream density measurements. The gap-rate–width data are in good agreement with the CDM-predicted relation. The high density of gaps in the narrow streams requires a total halo population of 10 5 sub-halos above a minimum mass of 10 5 M ☉ .

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0020.000

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations151
Published2012
Admission routes1
Has abstractyes

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