Homogeneous analysis of globular clusters from the APOGEE survey with the BACCHUS code
Bibliographic record
Abstract
Aims. We seek to provide abundances of a large set of light and neutron-capture elements homogeneously analyzed that cover a wide range of metallicity to constrain globular cluster (GC) formation and evolution models. Methods. We analyzed a large sample of 885 GCs giants from the SDSS IV-Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. We used the Cannon results to separate the red giant branch and asymptotic giant branch stars, not only allowing for a refinement of surface gravity from isochrones, but also providing an independent H-band spectroscopic method to distinguish stellar evolutionary status in clusters. We then used the Brussels Automatic Code for Characterizing High accUracy Spectra (BACCHUS) to derive metallicity, microturbulence, macroturbulence, many light-element abundances, and the neutron-capture elements Nd and Ce for the first time from the APOGEE GCs data. Results. Our independent analysis helped us to diagnose issues regarding the standard analysis of the APOGEE DR14 for low-metallicity GC stars. Furthermore, while we confirm most of the known correlations and anticorrelation trends (Na-O, Mg-Al, C-N), we discover that some stars within our most metal-poor clusters show an extreme Mg depletion and some Si enhancement. At the same time, these stars show some relative Al depletion, displaying a turnover in the Mg-Al diagram. These stars suggest that Al has been partially depleted in their progenitors by very hot proton-capture nucleosynthetic processes. Furthermore, we attempted to quantitatively correlate the spread of Al abundances with the global properties of GCs. We find an anticorrelation of the Al spread against clusters metallicity and luminosity, but the data do not allow us to find clear evidence of a dependence of N against metallicity in the more metal-poor clusters. Conclusions. Large and homogeneously analyzed samples from ongoing spectroscopic surveys unveil unseen chemical details for many clusters, including a turnover in the Mg-Al anticorrelation, thus yielding new constrains for GCs formation/evolution models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".