Not too big, not too small: the dark haloes of the dwarf spheroidals in the Milky Way
Bibliographic record
Abstract
We present a new analysis of the Aquarius simulations done in combination with a semi-analytic galaxy formation model. Our goal is to establish whether the subhaloes present in Λ cold dark matter simulations of Milky Way (MW) like systems could host the dwarf spheroidal (dSph) satellites of our Galaxy. Our analysis shows that, contrary to what has been assumed in most previous work, the mass profiles of subhaloes are generally not well fitted by Navarro–Frenk–White models but that Einasto profiles are preferred. We find that for shape parameters α = 0.2–0.5 and vmax = 10–30 km s−1 there is very good correspondence with the observational constraints obtained for the nine brightest dSphs of the MW. However, to explain the internal dynamics of these systems as well as the number of objects of a given circular velocity the total mass of the MW should be ∼8 × 1011 M⊙, a value that is in agreement with many recent determinations, and at the low-mass end of the range explored by the Aquarius simulations. Our simulations show important scatter in the number of bright satellites, even when the Aquarius MW-like hosts are scaled to a common mass, and we find no evidence for a missing population of massive subhaloes in the Galaxy. This conclusion is also supported when we examine the dynamics of the satellites of M31.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".