Chemical abundances for the outer halo cluster Pal 4 from co-added high-resolution spectroscopy
Bibliographic record
Abstract
Context. Chemical element abundances for distant Galactic globular clusters (GCs) hold important clues to the origin of the Milky Way halo and its substructures.Aims. We study the chemical composition of red giant stars in Pal 4 – one of the most remote GCs in the Milky Way – and compare our abundance measurements to those for both low surface brightness dwarf galaxies, and GCs in the inner and the outer halo.Methods. By co-adding high-resolution, low-S/N Keck/HIRES spectra of 19 stars along the red giant branch, we estimate chemical abundance ratios of 20 α-, iron peak-, and neutron-capture elements. Our method gives total uncertainties on most element-to-iron ratios of typically 0.2 dex.Results. We measure [Fe/H] = -1.41 ± 0.04 (statistical) ± 0.17 (systematic) and an α-enhancement of [α/Fe] = +0.38 ± 0.11 dex, which is consistent with the canonical value of ~+0.4 dex found for Galactic halo field stars and most halo GCs at this metallicity. Although Pal 4 has higher enhancements in the heavier elements with respect to the halo, the majority of the element ratios are, within the measurement errors, consistent with those for local halo field stars. We find, however, evidence for a lower [Mg/Ca] ratio than in other halo clusters.Conclusions. Based on the available evidence, we conclude that the material from which Pal 4 and the Galactic halo formed experienced similar enrichment processes, despite the apparently younger age of this cluster. Within the limitations of our methodology we find no significant indication of an iron spread, as is typical of genuine GCs of the Milky Way. However, abundance ratios for individual stars in Pal 4 and other distant satellites are urgently needed to understand the relationship, if any, between remote GCs and other halo substructures (i.e., luminous and ultra-faint dwarf spheroidal galaxies).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".