The orbital ellipticity of satellite galaxies and the mass of the Milky Way
Bibliographic record
Abstract
We use simulations of Milky Way-sized dark matter haloes from the Aquarius Project to investigate the orbits of substructure haloes likely, according to a semi-analytic galaxy formation model, to host luminous satellites. These tend to populate the most massive subhaloes and are on more radial orbits than the majority of subhaloes found within the halo virial radius. One reason for this (mild) kinematic bias is that many low-mass subhaloes have apocentres that exceed the virial radius of the host; they are thus excluded from subhalo samples identified within the virial boundary, reducing the number of subhaloes on radial orbits. Two other factors contributing to the difference in orbital shape between dark and luminous subhaloes are their dynamical evolution after infall, which affects more markedly low-mass (dark) subhaloes, and a weak dependence of ellipticity on the redshift of first infall. The ellipticity distribution of luminous satellites exhibits little halo-to-halo scatter, and it may therefore be compared fruitfully with that of Milky Way satellites. Since the latter depends sensitively on the total mass of the Milky Way we can use the predicted distribution of satellite ellipticities to place constraints on this important parameter. Using the latest estimates of position and velocity of dwarfs compiled from the literature, we find that the most likely Milky Way mass lies in the range 6 × 1011 M⊙ < M200 < 3.1 × 1012 M⊙, with a best-fitting value of M200 = 1.1 × 1012 M⊙. This value is consistent with Milky Way mass estimates based on dynamical tracers or the timing argument.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".