Tracing the Milky Way: calibrating chemical ages with high-precision <i>Kepler</i> data
Bibliographic record
Abstract
ABSTRACT Chemical clocks offer a powerful tool for estimating stellar ages from spectroscopic surveys. We present a new detailed spectroscopic analysis of 68 Kepler red giant stars to provide a suite of high-precision abundances along with asteroseismic ages with better than 10 per cent precision from individual mode frequencies. We obtained several chemical clocks as ratios between s-process elements (Y, Zr, Ba, La, and Ce) and $\alpha$-elements (Mg, Ca, Si, Al, and Ti). Our data show that [Ce/Mg] and [Zr/Ti] display a remarkably tight correlation with stellar ages, with abundance dispersions of 0.08 and 0.01 dex, respectively, and below 3 Gyr in ages, across the entire Galactic chronochemical history. While improving the precision floor of spectroscopic surveys is critical for broadening the scope and applicability of chemical clocks, the intrinsic accuracy of our relations – enabled by high-resolution chemical abundances and stellar ages in our sample – allows us to draw meaningful conclusions about age trends across stellar populations. By applying our relations to the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Gaia-ESO surveys, we are able to differentiate the low- and high-$\alpha$ sequences in age, recover the age–metallicity relation, observe the disc flaring of the Milky Way, and identify a population of old metal-rich stars.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".