Red giant masses and ages derived from carbon and nitrogen abundances
Bibliographic record
Abstract
We show that the masses of red giant stars can be well predicted from their photospheric carbon and nitrogen abundances, in conjunction with their spectroscopic stellar labels log g, Teff, and [Fe/H]. This is qualitatively expected from mass-dependent post-main-sequence evolution. We here establish an empirical relation between these quantities by drawing on 1475 red giants with asteroseismic mass estimates from Kepler that also have spectroscopic labels from Apache Point Observatory Galactic Evolution Experiment (APOGEE) DR12. We assess the accuracy of our model, and find that it predicts stellar masses with fractional rms errors of about 14 per cent (typically 0.2 M⊙). From these masses, we derive ages with rms errors of 40 per cent. This empirical model allows us for the first time to make age determinations (in the range 1–13 Gyr) for vast numbers of giant stars across the Galaxy. We apply our model to ∼52 000 stars in APOGEE DR12, for which no direct mass and age information was previously available. We find that these estimates highlight the vertical age structure of the Milky Way disc, and that the relation of age with [α/M] and metallicity is broadly consistent with established expectations based on detailed studies of the solar neighbourhood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".