The contribution of N-rich stars to the Galactic stellar halo using APOGEE red giants
Bibliographic record
Abstract
ABSTRACT The contribution of dissolved globular clusters (GCs) to the stellar content of the Galactic halo is a key constraint on models for GC formation and destruction, and the mass assembly history of the Milky Way. Earlier results from APOGEE pointed to a large contribution of destroyed GCs to the stellar content of the inner halo, by as much as 25 ${{\ \rm per\ cent}}$, which is an order of magnitude larger than previous estimates for more distant regions of the halo. We set out to measure the ratio between nitrogen-rich (N-rich) and normal halo field stars, as a function of distance, by performing density modelling of halo field populations in APOGEE DR16. Our results show that at 1.5 kpc from the Galactic Centre, N-rich stars contribute a much higher 16.8$^{+10.0}_{-7.0}\, {{\ \rm per\ cent}}$ fraction to the total stellar halo mass budget than the 2.7$^{+1.0}_{-0.8}\, {{\ \rm per\ cent}}$ ratio contributed at 10 kpc. Under the assumption that N-rich stars are former GC members that now reside in the stellar halo field, and assuming the ratio between first and second population GC stars being 1:2, we estimate a total contribution from disrupted GC stars of the order of 27.5$^{+15.4}_{-11.5}\, {{\ \rm per\ cent}}$ at r = 1.5 kpc and 4.2$^{+1.5}_{-1.3}\, {{\ \rm per\ cent}}$ at r = 10 kpc. Furthermore, since our methodology requires fitting a density model to the stellar halo, we integrate such density within a spherical shell from 1.5 to 15 kpc in radius, and find a total stellar mass arising from dissolved and/or evaporated GCs of MGC,total = 9.6$^{+4.0}_{-2.6}\, \times$ 107 M⊙.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".