Determination of Sodium Abundance Ratio from Low-resolution Stellar Spectra and Its Applications
Bibliographic record
Abstract
Abstract We present a method to determine sodium abundance ratios ([Na/Fe]) using the Na i D doublet lines in low-resolution (R ∼ 2000) stellar spectra. As stellar Na i D lines are blended with those produced by the interstellar medium, we developed a technique for removing the interstellar Na i D lines using the relationship between extinction, which is proportional to E(B − V), and the equivalent width of the interstellar Na i D absorption lines. When measuring [Na/Fe], we also considered corrections for nonlocal thermodynamic equilibrium (NLTE) effects. Comparisons with data from high-resolution spectroscopic surveys suggest that the expected precision of [Na/Fe] from low-resolution spectra is better than 0.3 dex for stars with [Fe/H] > −3.0. We also present a simple application employing the estimated [Na/Fe] values for a large number of stellar spectra from the Sloan Digital Sky Survey (SDSS). After classifying the SDSS stars into Na-normal, Na-high, and Na-extreme, we explore their relation to stars in Galactic globular clusters (GCs). We find that while the Na-high SDSS stars exhibit a similar metallicity distribution function (MDF) to that of the GCs, indicating that the majority of such stars may have originated from GC debris, the MDF of the Na-normal SDSS stars follows that of typical disk and halo stars. As there is a high fraction of carbon-enhanced metal-poor stars among the Na-extreme stars, they may have a non-GC origin, perhaps due to mass-transfer events from evolved binary companions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".