Constraining the Galaxy's dark halo with RAVE stars
Bibliographic record
Abstract
We use the kinematics of $\sim200\,000$ giant stars that lie within $\sim 1.5$ kpc of the plane to measure the vertical profile of mass density near the Sun. We find that the dark mass contained within the isodensity surface of the dark halo that passes through the Sun ($(6\pm0.9)\times10^{10}\,\mathrm{M_\odot}$), and the surface density within $0.9$ kpc of the plane ($(69\pm10)\,\mathrm{M_\odot\,pc^{-2}}$) are almost independent of the (oblate) halo's axis ratio $q$. If the halo is spherical, 46 per cent of the radial force on the Sun is provided by baryons, and only 4.3 per cent of the Galaxy's mass is baryonic. If the halo is flattened, the baryons contribute even less strongly to the local radial force and to the Galaxy's mass. The dark-matter density at the location of the Sun is $0.0126\,q^{-0.89}\,\mathrm{M_\odot\,pc^{-3}}=0.48\,q^{-0.89}\,\mathrm{GeV\,cm^{-3}}$. When combined with other literature results we find hints for a mildly oblate dark halo with $q \simeq 0.8$. Our value for the dark mass within the solar radius is larger than that predicted by cosmological dark-matter-only simulations but in good agreement with simulations once the effects of baryonic infall are taken into account. Our mass models consist of three double-exponential discs, an oblate bulge and a Navarro-Frenk-White dark-matter halo, and we model the dynamics of the RAVE stars in the corresponding gravitational fields by finding distribution functions $f(\mathbf{J})$ that depend on three action integrals. Statistical errors are completely swamped by systematic uncertainties, the most important of which are the distance to the stars in the photometric and spectroscopic samples and the solar distance to the Galactic centre. Systematics other than the flattening of the dark halo yield overall uncertainties $\sim 15$ per cent.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".