Evidence of deep mixing in IRS 7, a cool massive supergiant member of the Galactic nuclear star cluster
Bibliographic record
Abstract
ABSTRACT The centre of the Milky Way contains stellar populations spanning a range in age and metallicity, with a recent star formation burst producing young and massive stars. Chemical abundances in the most luminous stellar member of the nuclear star cluster (NSC), IRS 7, are presented for 19F, 12C, 13C, 14N, 16O, 17O, and Fe from a local thermodynamic equilibrium analysis based on spherical modelling and radiative transfer with a 25-M⊙ model atmosphere, whose chemistry was tailored to the derived photospheric abundances. We find IRS 7 to be depleted heavily in both 12C (∼–0.8 dex) and 16O (∼–0.4 dex), while exhibiting an extremely enhanced 14N abundance (∼+1.1 dex), which are isotopic signatures of the deep mixing of CNO-cycled material to the stellar surface. The 19F abundance is also heavily depleted by ∼1 dex relative to the baseline fluorine of the NSC, providing evidence that fluorine along with carbon constrain the nature of the deep mixing in this very luminous supergiant. The abundances of the minor isotopes 13C and 17O are also derived, with ratios of 12C/13C ∼ 5.3 and 16O/17O ∼ 525. The derived abundances for IRS 7, in conjunction with previous abundance results for massive stars in the NSC, are compared with rotating and non-rotating models of massive stars and it is found that the IRS 7 abundances overall follow the behaviour predicted by stellar models. The depleted fluorine abundance in IRS 7 illustrates, for the first time, the potential of using the 19F abundance as a mixing probe in luminous red giants.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".