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The dark matter haloes of dwarf galaxies: a challenge for the Λ cold dark matter paradigm?

2012· article· en· W1750329983 on OpenAlexaff

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDwarf galaxyDwarf galaxy problemDark galaxyDark matterCold dark matterGalaxyGalaxy rotation curveDark matter haloDwarf spheroidal galaxy

Abstract

fetched live from OpenAlex

The cold dark matter halo mass function is much steeper than the galaxy stellar mass function on galactic and subgalactic scales. This difference is usually reconciled by assuming that the galaxy formation efficiency drops sharply with decreasing halo mass, so that virtually no dwarf galaxies form in haloes less massive than ∼1010 M⊙. In turn, this implies that, at any given radius, the dark mass enclosed by a galaxy must exceed a certain minimum. We use rotation curves of dwarf galaxies compiled from the literature to explore whether their enclosed mass is consistent with these constraints. We find that almost one-half of the dwarfs in our sample with stellar mass in the range of 106 < Mgal/M⊙ < 107 are at odds with this restriction: either they live in haloes with masses substantially below 1010 M⊙ or there is a mechanism capable of reducing the dark mass enclosed by some of the faintest dwarfs. Neither possibility is easily accommodated within the standard Λ cold dark matter scenario. Extending galaxy formation to haloes well below 1010 M⊙ would lead to large numbers of dwarf galaxies in excess of current estimates; at the same time, the extremely low stellar mass of the systems involved makes it unlikely that baryonic effects can reduce their dark matter content. Resolving this challenge seems to require new insights into dwarf galaxy formation, or perhaps a radical revision of the prevailing 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 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 score0.021
Threshold uncertainty score0.618

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations137
Published2012
Admission routes1
Has abstractyes

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