The missing massive satellites of the Milky Way
Bibliographic record
Abstract
Recent studies suggest that only three of the 12 brightest satellites of the Milky Way (MW) inhabit dark matter haloes with maximum circular velocity, Vmax, exceeding ∼30 km s−1. This is in apparent contradiction with the Λ cold dark matter (CDM) simulations of the Aquarius Project, which suggest that MW-sized haloes should have at least eight subhaloes with Vmax > 30 km s−1. The absence of luminous satellites in such massive subhaloes is thus puzzling and may present a challenge to the ΛCDM paradigm. We note, however, that the number of massive subhaloes depends sensitively on the (poorly known) virial mass of the MW, and that their scarcity makes estimates of their abundance from a small simulation set like Aquarius uncertain. We use the Millennium Simulation series and the invariance of the scaled subhalo velocity function (i.e. the number of subhaloes as a function of ν, the ratio of the subhalo Vmax to the host halo virial velocity, V200) to secure improved estimates of the abundance of rare massive subsystems. In the range 0.1 < ν < 0.5, Nsub(>ν) is approximately Poisson distributed about an average given by 〈Nsub〉 = 10.2 (ν/0.15)−3.11. This is slightly lower than that in Aquarius haloes, but consistent with recent results from the Phoenix Project. The probability that a ΛCDM halo has three or fewer subhaloes with Vmax above some threshold value, Vth, is then straightforward to compute. It decreases steeply both with decreasing Vth and with increasing halo mass. For Vth = 30 km s−1, ∼40 per cent of Mhalo = 1012 M⊙ haloes pass the test; fewer than ∼5 per cent do so for Mhalo ≳ 2 × 1012 M⊙ and the probability effectively vanishes for Mhalo ≳ 3 × 1012 M⊙. Rather than a failure of ΛCDM, the absence of massive subhaloes might simply indicate that the MW is less massive than is commonly thought.
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 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 | 0.001 | 0.003 |
| 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".