Stellar Properties for a Comprehensive Collection of Star-forming Regions in the SDSS APOGEE-2 Survey*
Bibliographic record
Abstract
Abstract The Sloan Digital Sky Survey IV APOGEE-2 primary science goal was to observe red giant stars throughout the Galaxy to study its dynamics, morphology, and chemical evolution. The APOGEE instrument, a high-resolution 300-fiber H-band (1.55–1.71 μm) spectrograph, is also ideal to study other stellar populations in the Galaxy, among which are a number of star-forming regions and young open clusters. We present the results of the determination of six stellar properties (T eff, log g , [Fe/H], L/L ⊙, M/M ⊙, and age) for a sample that is composed of 3360 young stars, of subsolar to supersolar types, in 16 Galactic star formation and young open cluster regions. Those sources were selected by using a clustering method that removes most of the field contamination. Samples were also refined by removing targets affected by various systematic effects of the parameter determination. The final samples are presented in a comprehensive catalog that includes all six estimated parameters. This overview study also includes parameter spatial distribution maps for all regions and Hertzsprung–Russell ( log L / L ⊙ vs. T eff) diagrams. This study serves as a guide for detailed studies on individual regions and paves the way for the future studies on the global properties of stars in the pre-main-sequence phase of stellar evolution using more robust samples.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".