On the Use of Field RR Lyrae as Galactic Probes. III. The α-element Abundances*
Bibliographic record
Abstract
Abstract We provide the largest and most homogeneous sample of α-element (Mg, Ca, Ti) and iron abundances for field RR Lyrae (RRLs; 162 variables) by using high-resolution spectra. The current measurements were complemented with similar abundances available in the literature for 46 field RRLs brought to our metallicity scale. We ended up with a sample of old (t ≥ 10 Gyr), low-mass stellar tracers (208 RRLs: 169 fundamental, 38 first overtone, and 1 mixed mode) covering 3 dex in iron abundance (−3.00 ≤ [Fe/H] ≤ 0.24). We found that field RRLs are ∼0.3 dex more α poor than typical halo tracers in the metal-rich regime ([Fe/H] ≥ −1.2), while in the metal-poor regime ([Fe/H] ≤ −2.2) they seem to be on average ∼0.1 dex more α enhanced. This is the first time that the depletion in α elements for solar iron abundances is detected on the basis of a large, homogeneous, and coeval sample of old stellar tracers. Interestingly, we also detected a close similarity in the [α/Fe] trend between α-poor, metal-rich RRLs and red giants (RGs) in the Sagittarius dwarf galaxy as well as between α-enhanced, metal-poor RRLs and RGs in ultrafaint dwarf galaxies. These results are supported by similar elemental abundances for 46 field horizontal branch stars. These stars share with RRLs the same evolutionary phase and the same progenitors. This evidence further supports the key role that old stellar tracers play in constraining the early chemical enrichment of the halo and, in particular, in investigating the impact that dwarf galaxies have had in the mass assembly of the Galaxy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".