Weighing the stellar constituents of the galactic halo with APOGEE red giant stars
Bibliographic record
Abstract
ABSTRACT The stellar mass in the halo of the Milky Way is notoriously difficult to determine, owing to the paucity of its stars in the solar neighbourhood. With tentative evidence from Gaia that the nearby stellar halo is dominated by a massive accretion event – referred to as Gaia-Enceladus or Sausage – these constraints are now increasingly urgent. We measure the mass in kinematically selected mono-abundance populations (MAPs) of the stellar halo between −3 < [Fe/H] < −1 and 0.0 < [Mg/Fe] < 0.4 using red giant star counts from APOGEE DR14. We find that MAPs are well fit by single power laws on triaxial ellipsoidal surfaces, and we show that that the power-law slope α changes such that high [Mg/Fe] populations have α ∼ 4, whereas low [Mg/Fe] MAPs are more extended with shallow slopes, α ∼ 2. We estimate the total stellar mass to be $M_{*,\mathrm{tot}} = 1.3^{+0.3}_{-0.2}\times 10^{9}\ \mathrm{M_{\odot}}$, of which we estimate ${\sim}0.9^{+0.2}_{-0.1} \times 10^{9}\ \mathrm{M_{\odot}}$ to be accreted. We estimate that the mass of accreted stars with e > 0.7 is M*,accreted, e > 0.7 = 3 ± 1 (stat.) ± 1 (syst.) × 108 M⊙, or ${\sim}30{-}50{{\ \rm per\ cent}}$ of the accreted halo mass. If the majority of these stars are the progeny of a massive accreted dwarf, this places an upper limit on its stellar mass, and implies a halo mass for the progenitor of ∼1010.2 ± 0.2 M⊙. This constraint not only shows that the Gaia-Enceladus/Sausage progenitor may not be as massive as originally suggested, but that the majority of the Milky Way stellar halo was accreted. These measurements are an important step towards fully reconstructing the assembly history of the Milky Way.
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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 | 0.002 | 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 | 0.000 |
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".