Revealing the Chemical Structure of the Magellanic Clouds with APOGEE. I. Calculating Individual Stellar Ages of RGB Stars in the Large Magellanic Cloud
Bibliographic record
Abstract
Stellar ages are critical for understanding the temporal evolution of a galaxy. We calculate the ages of over 6000 red giant branch stars in the Large Magellanic Cloud (LMC) observed with SDSS-IV / APOGEE-S. Ages are derived using multi-band photometry, spectroscopic parameters (T$_\text{eff}$, $\log{g}$, [Fe/H], and [$α$/Fe]) and stellar isochrones and the assumption that the stars lie in a thin inclined plane to get accurate distances. The isochrone age and extinction are varied until a best match is found for the observed photometry. We perform validation using the APOKASC sample, which has asteroseismic masses and accurate ages, and find that our uncertainties are $\sim$20% and range from $\sim$1$-$3 Gyr for the calculated age values. Here we present the LMC age map as well as the age-radius relation and an accurate age-metallicity relation (AMR). The age map and age-radius relation reveal that recent star formation in the galaxy was more centrally located and that there is a slight dichotomy between the north and south with the northern fields being slightly younger. The northern fields that cover a known spiral arm have median ages of $\gtrsim$ 2 Gyr, which is the time when an interaction with the SMC is suggested to have happened. The AMR is mostly flat especially for older ages although recently (about 2.0-2.5 Gyr ago) there is an increase in the median [Fe/H]. Based on the time frame, this might also be attributed to the close interaction between the LMC and SMC.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".