On the Use of Field RR Lyrae as Galactic Probes. II. A New ΔS Calibration to Estimate Their Metallicity*
Bibliographic record
Abstract
Abstract We performed the largest and most homogeneous spectroscopic survey of field RR Lyraes (RRLs). We secured ≈6300 high-resolution (HR, R ∼ 35,000) spectra for 143 RRLs (111 fundamental, RRab; 32 first-overtone, RRc). The atmospheric parameters were estimated by using the traditional approach and the iron abundances were measured by using an LTE line analysis. The resulting iron distribution shows a well-defined metal-rich tail approaching solar iron abundance. This suggests that field RRLs experienced a complex chemical enrichment in the early halo formation. We used these data to develop a new calibration of the ΔS method. This diagnostic, based on the equivalent widths of Ca ii K and three Balmer (H δ,γ,β ) lines, traces the metallicity of RRLs. For the first time, the new empirical calibration: (i) includes spectra collected over the entire pulsation cycle; (ii) includes RRc variables; (iii) relies on spectroscopic calibrators covering more than three dex in iron abundance; and (iv) provides independent calibrations based on one/two/three Balmer lines. The new calibrations were applied to a data set of both SEGUE-SDSS and degraded HR spectra totalling 6451 low-resolution (R ∼ 2000) spectra for 5001 RRLs (3439 RRab, 1562 RRc). This resulted in an iron distribution with a median η = −1.55 ± 0.01 and σ = 0.51 dex, in good agreement with literature values. We also found that RRc are 0.10 dex more metal-poor than RRab variables, and have a distribution with a smoother metal-poor tail. This finding supports theoretical prescriptions suggesting a steady decrease in the RRc number when moving from metal-poor to metal-rich stellar environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".