Zero-age horizontal branch models for −2.5 ≤ [Fe/H] ≤−0.5 and their implications for the apparent distance moduli of globular clusters
Bibliographic record
Abstract
ABSTRACT Grids of zero-age horizontal branch (ZAHB) models are presented, along with a suitable interpolation code, for −2.5 ≤ [Fe/H] ≤ −0.5, in steps of 0.2 dex, assuming Y = 0.25 and 0.29, [O/Fe] = +0.4 and +0.6, and [m/Fe] = +0.4 for all of the other α-elements. The HB populations of 37 globular clusters (GCs) are fitted to these ZAHBs to derive their apparent distance moduli, (m − M)V. With few exceptions, the best estimates of their reddenings from dust maps are adopted. The distance moduli are constrained using the prediction that (MF606W − MF814W)0 colours of metal-poor, main-sequence stars at $M_{F606W} \mathrel {\rm{{\gt }\lower.5 ex\rm{\sim }}}5.0$ have very little sensitivity to [Fe/H]. Intrinsic (MF336W − MF606W)0 colours of blue HB stars, which provide valuable connections between GCs with exclusively blue HBs and other clusters of similar metallicity that also have red HB components, limit the uncertainties of relative (m − M)V values to within ±0.03–0.04 mag. The ZAHB-based distances agree quite well with the distances derived by Baumgardt & Vasiliev. Their implications for GC ages are briefly discussed. Stellar rotation and mass loss appear to be more important than helium abundance variations in explaining the colour–magnitude diagrams of second-parameter GCs (those with anomalously very blue HBs for their metallicities).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".