The milky way total mass profile as inferred from Gaia DR2
Bibliographic record
Abstract
ABSTRACT We determine the Milky Way (MW) mass profile inferred from fitting physically motivated models to the Gaia DR2 Galactic rotation curve and other data. Using various hydrodynamical simulations of MW-mass haloes, we show that the presence of baryons induces a contraction of the dark matter (DM) distribution in the inner regions, r ≲ 20 kpc. We provide an analytic expression that relates the baryonic distribution to the change in the DM halo profile. For our galaxy, the contraction increases the enclosed DM halo mass by factors of roughly 1.3, 2, and 4 at radial distances of 20, 8, and 1 kpc, respectively compared to an uncontracted halo. Ignoring this contraction results in systematic biases in the inferred halo mass and concentration. We provide a best-fitting contracted NFW halo model to the MW rotation curve that matches the data very well.1 The best-fit has a DM halo mass, $M_{200}^{\rm DM}=0.97_{-0.19}^{+0.24}\times 10^{12}\,\mathrm{M}_\odot$, and concentration before baryon contraction of $9.4_{-2.6}^{+1.9}$, which lie close to the median halo mass–concentration relation predicted in ΛCDM. The inferred total mass, $M_{200}^{\rm total}=1.08_{-0.14}^{+0.20} \times 10^{12}\,\mathrm{M}_\odot$, is in good agreement with recent measurements. The model gives an MW stellar mass of $5.04_{-0.52}^{+0.43}\times 10^{10}\,\mathrm{M}_\odot$ and infers that the DM density at the Solar position is $\rho _{\odot }^{\rm DM}=8.8_{-0.5}^{+0.5}\times 10^{-3}\,\mathrm{M}_\odot \,\mathrm{pc}^{-3}\equiv 0.33_{-0.02}^{+0.02}\,\rm {GeV}\,\rm {cm}^{-3}$. The rotation curve data can also be fitted with an uncontracted NFW halo model, but with very different DM and stellar parameters. The observations prefer the physically motivated contracted NFW halo, but the measurement uncertainties are too large to rule out the uncontracted NFW halo.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".