The dark matter haloes of dwarf galaxies: a challenge for the Λ cold dark matter paradigm?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".