SPECTROSCOPIC DETERMINATION OF MASSES (AND IMPLIED AGES) FOR RED GIANTS
Bibliographic record
Abstract
ABSTRACT The mass of a star is arguably its most fundamental parameter. For red giant stars, tracers luminous enough to be observed across the Galaxy, mass implies a stellar evolution age. It has proven to be extremely difficult to infer ages and masses directly from red giant spectra using existing methods. From the Kepler and apogee surveys, samples of several thousand stars exist with high-quality spectra and asteroseismic masses. Here we show that from these data we can build a data-driven spectral model using The Cannon , which can determine stellar masses to ∼0.07 dex from apogee dr12 spectra of red giants; these imply age estimates accurate to ∼0.2 dex (40%). We show that The Cannon constrains these ages foremost from spectral regions with CN absorption lines, elements whose surface abundances reflect mass-dependent dredge-up. We deliver an unprecedented catalog of 70,000 giants (including 20,000 red clump stars) with mass and age estimates, spanning the entire disk (from the Galactic center to kpc). We show that the age information in the spectra is not simply a corollary of the birth-material abundances and , and that, even within a monoabundance population of stars, there are age variations that vary sensibly with Galactic position. Such stellar age constraints across the Milky Way open up new avenues in Galactic archeology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".