On the Use of Field RR Lyrae As Galactic Probes: IV. New Insights Into and Around the Oosterhoff Dichotomy*
Bibliographic record
Abstract
Abstract We discuss the largest and most homogeneous spectroscopic data set of field RR Lyrae variables (RRLs) available to date. We estimated abundances using both high-resolution and low-resolution (ΔS method) spectra for fundamental (RRab) and first overtone (RRc) RRLs. The iron abundances for 7941 RRLs were supplemented with similar estimates that are available in the literature, ending up with 9015 RRLs (6150 RRab, 2865 RRc). The metallicity distribution shows a mean value of 〈[Fe/H]〉 = −1.51 ± 0.01, and σ(standard deviation) = 0.41 dex with a long metal-poor tail approaching [Fe/H] ≃ − 3 and a sharp metal-rich tail approaching solar iron abundance. The RRab variables are more metal-rich (〈[Fe/H]〉ab = −1.48 ± 0.01, σ = 0.41 dex) than RRc variables (〈[Fe/H]〉c = −1.58 ± 0.01, σ = 0.40 dex). The relative fraction of RRab variables in the Bailey diagram (visual amplitude versus period) located along the short-period (more metal-rich) and the long-period (more metal-poor) sequences are 80% and 20%, while RRc variables display an opposite trend, namely 30% and 70%, respectively. We found that the pulsation period of both RRab and RRc variables steadily decreases when moving from the metal-poor to the metal-rich regime. The visual amplitude shows the same trend, but RRc amplitudes are almost two times more sensitive than RRab amplitudes to metallicity. We also investigated the dependence of the population ratio (N c /Ntot) of field RRLs on the metallicity and we found that the distribution is more complex than in globular clusters. The population ratio steadily increases from ∼0.25 to ∼0.36 in the metal-poor regime, it decreases from ∼0.36 to ∼0.18 for −1.8 ≤ [Fe/H] ≤ −0.9 and it increases to a value of ∼0.3 approaching solar iron abundance.
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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 | 0.004 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".