Masses and ages for metal-poor stars
Bibliographic record
Abstract
Context. Very metal-poor halo stars are the best candidates for being among the oldest objects in our Galaxy. Samples of halo stars with age determination and detailed chemical composition measurements provide key information for constraining the nature of the first stellar generations and the nucleosynthesis in the metal-poor regime. Aims. Age estimates are very uncertain and are available for only a small number of metal-poor stars. We present the first results of a pilot programme aimed at deriving precise masses, ages, and chemical abundances for metal-poor halo giants using asteroseismology and high-resolution spectroscopy. Methods. We obtained high-resolution UVES spectra for four metal-poor RAVE stars observed by the K2 satellite. Seismic data obtained from K2 light curves helped improve spectroscopic temperatures, metallicities, and individual chemical abundances. Mass and ages were derived using the code PARAM, investigating the effects of different assumptions (e.g. mass loss and [α/Fe]-enhancement). Orbits were computed using Gaia DR2 data. Results. The stars are found to be normal metal-poor halo stars (i.e. non C-enhanced), and an abundance pattern typical of old stars (i.e. α and Eu-enhanced), and have masses in the 0.80−1.0 M⊙ range. The inferred model-dependent stellar ages are found to range from 7.4 Gyr to 13.0 Gyr with uncertainties of ∼30%−35%. We also provide revised masses and ages for metal-poor stars with Kepler seismic data from the APOGEE survey and a set of M4 stars. Conclusions. The present work shows that the combination of asteroseismology and high-resolution spectroscopy provides precise ages in the metal-poor regime. Most of the stars analysed in the present work (covering the metallicity range of [Fe/H] ∼ −0.8 to −2 dex) are very old >9 Gyr (14 out of 19 stars), and all of the stars are older than >5 Gyr (within the 68 percentile confidence level).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".