Galactic Mass Estimates Using Dwarf Galaxies as Kinematic Tracers
Bibliographic record
Abstract
Abstract New mass estimates and cumulative mass profiles with Bayesian credible regions for the Milky Way (MW) are found using the Galactic Mass Estimator (GME) code and dwarf galaxy (DG) kinematic data from multiple sources. GME takes a hierarchical Bayesian approach to simultaneously estimate the true positions and velocities of the DGs, their velocity anisotropy, and the model parameters for the Galaxy’s total gravitational potential. In this study, we incorporate meaningful prior information from past studies and simulations. The prior distributions for the physical model are informed by the results of Eadie & Jurić, who used globular clusters instead of DGs, as well as by the subhalo distributions of the Ananke Gaia-like surveys from Feedback in Realistic Environments-2 cosmological simulations (see Sanderson et al.). Using DGs beyond 45 kpc, we report median and 95% credible region estimates for r 200 = 212.8 (191.12, 238.44) kpc, and for the total enclosed mass M 200 = 1.19 (0.87, 1.68) × 1012 M ⊙ (adopting Δ c = 200). Median mass estimates at specific radii are also reported (e.g., M (< 50 kpc) = 0.52 × 1012 M ⊙ and M (100 kpc) = 0.78 × 1012 M ⊙). Estimates are comparable to other recent studies using Gaia DR2 and DGs, but notably different from the estimates of Eadie & Jurić. We perform a sensitivity analysis to investigate whether individual DGs and/or a more massive Large Magellanic Cloud on the order of 1011 M ⊙ may be affecting our mass estimates. We find possible supporting evidence for the idea that some DGs are affected by a massive LMC and are not in equilibrium with the MW.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".