THE STELLAR POPULATION STRUCTURE of the GALACTIC DISK
Bibliographic record
Abstract
The spatial structure of stellar populations with different chemical abundances in the Milky Way (MW) contains a wealth of information on Galactic evolution over cosmic time. We use data on 14,699 red-clump stars from the APOGEE survey, covering 4 kpc∼ R≳15 kpc, to determine the structure of mono-abundance populations (MAPs)-stars in narrow bins in [α/Fe] and [Fe H]-accounting for the complex effects of the APOGEE selection function and the spatially variable dust obscuration. We determine that all MAPs with enhanced [α/Fe] are centrally concentrated and are well-described as exponentials with a scale length of 2.2 ± 0.2 kpc over the whole radial range of the disk. We discover that the surface-density profiles of low-[α/Fe] MAPs are complex: they do not monotonically decrease outwards, but rather display a peak radius ranging from ≈5 to ≈13 kpc at low [Fe H]. The extensive radial coverage of the data allows us to measure radial trends in the thickness of each MAP. While high-[α/Fe] MAPs have constant scale heights, low-[α/Fe] MAPs flare. We confirm, now with highprecision abundances, previous results that each MAP contains only a single vertical scale height and that low- [Fe H], low-[α/Fe] and high-[Fe H], high-[α/Fe] MAPs have intermediate (hZ≈300600 pc) scale heights that smoothly bridge the traditional thin- and thick-disk divide. That the high-[α/Fe], thick disk components do not flare is strong evidence against their thickness being caused by radial migration. The correspondence between the radial structure and chemical-enrichment age of stellar populations is clear confirmation of the inside-out growth of galactic disks. The details of these relations will constrain the variety of physical conditions under which stars form throughout the MW disk.
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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 | 0.000 |
| 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".