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Record W2109705950 · doi:10.48550/arxiv.1412.2748

Local Group galaxies emerge from the dark

2014· preprint· en· W2109705950 on OpenAlexaff
Till Sawala, Carlos S. Frenk, Azadeh Fattahi, Julio F. Navarro, R. G. Bower, Robert A. Crain, Claudio Dalla Vecchia, Michelle Furlong, John Helly, Adrian Jenkins, Kyle A. Oman, Matthieu Schaller, Joop Schaye, Tom Theuns, James W. Trayford, Simon D. M. White

Bibliographic record

VenueLiverpool John Moores University · 2014
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsPhysicsAstrophysicsDark galaxyDwarf galaxy problemLocal GroupGalaxy groupCold dark matterAstronomyDark matterGalaxyGalaxy formation and evolutionDark matter haloSatellite galaxyMilky WayHalo

Abstract

fetched live from OpenAlex

The "Lambda Cold Dark Matter" (LCDM) model of cosmic structure formation is eminently falsifiable: once its parameters are fixed on large scales, it becomes testable in the nearby Universe. Observations within our Local Group of galaxies, including the satellite populations of the Milky Way and Andromeda, appear to contradict LCDM predictions: there are far fewer satellite galaxies than dark matter halos (the "missing satellites" problem), galaxies seem to avoid the largest substructures (the "too big to fail" problem), and the brightest satellites appear to orbit their host galaxies on a thin plane (the "planes of satellites" problem). We present results from the first hydrodynamic simulations of the Local Group that match the observed abundance of galaxies. We find that when baryonic and dark matter are followed simultaneously in the context of a realistic galaxy formation model, all three "problems" are resolved within the LCDM paradigm.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.197
Teacher spread0.191 · 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 designTheoretical or conceptual
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

Citations29
Published2014
Admission routes1
Has abstractyes

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