NEW H-BAND STELLAR SPECTRAL LIBRARIES FOR THE SDSS-III/APOGEE SURVEY
Bibliographic record
Abstract
The Sloan Digital Sky Survey-III (SDSS-III) Apache Point Observatory Galactic Evolution Experiment (APOGEE) has obtained high-resolution ( R ∼ 22,500), high signal-to-noise ratio ( ) spectra in the H-band (∼1.5–1.7 μ m) for about 146,000 stars in the Milky Way galaxy. We have computed spectral libraries with effective temperature ( ) ranging from 3500 to 8000 K for the automated chemical analysis of the survey data. The libraries, used to derive stellar parameters and abundances from the APOGEE spectra in the SDSS-III data release 12 (DR12), are based on ATLAS9 model atmospheres and the ASS T spectral synthesis code. We present a second set of libraries based on MARCS model atmospheres and the spectral synthesis code Turbospectrum. The ATLAS9/ASS T ( = 3500–8000 K) and MARCS/Turbospectrum ( = 3500–5500 K) grids cover a wide range of metallicity (−2.5 [M/H] +0.5 dex), surface gravity (0 ≤ 5 dex), microturbulence (0.5 8 km s −1 ), carbon (−1 [C/M] +1 dex), nitrogen (−1 [N/M] +1 dex), and α -element (−1 [ α /M] +1 dex) variations, having thus seven dimensions. We compare the ATLAS9/ASS T and MARCS/Turbospectrum libraries and apply both of them to the analysis of the observed H-band spectra of the Sun and the K2 giant Arcturus, as well as to a selected sample of well-known giant stars observed at very high resolution. The new APOGEE libraries are publicly available and can be employed for chemical studies in the H-band using other high-resolution spectrographs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".