The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Bibliographic record
Abstract
Abstract We present a catalog of fundamental stellar properties for ∼7500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment, part of the Sloan Digital Sky Survey IV, and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically derived masses and radii. Additionally, we include kinematic information from Gaia. We investigate the multidimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low-metallicity stars (∼288 with [M/H] ≤ −1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α enhancement and position in the disk and identify those stars in the sample that are candidate components of the Gaia-Enceladus merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, ν max , color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.015 | 0.012 |
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".