Chemo-dynamical tagging in the outskirts: The origins of stellar substructures in the Magellanic Clouds
Bibliographic record
Abstract
We present the first detailed chemical analysis from APOGEE-2S observations of stars in six regions of recently discovered substructures in the outskirts of the Magellanic Clouds extending to 20° from the Large Magellanic Cloud (LMC) center. We also present, for the first time, the metallicity and α-abundance radial gradients of the LMC and the Small Magellanic Cloud (SMC) out to 11° and 6°, respectively. Our chemical tagging includes 13 species including light, α-, and Fe-peak elements. We find that the abundances of all of these chemical elements in stars populating two regions in the northern periphery, along the northern “stream-like” feature, show good agreement with the chemical patterns of the LMC, and thus likely have an LMC origin. For substructures located in the southern periphery of the LMC we find more complex chemical and kinematical signatures, indicative of a mix of LMC-like and SMC-like populations. The southern region closest to the LMC shows better agreement with the LMC, whereas that closest to the SMC shows a much better agreement with the SMC chemical pattern. When combining this information with 3D kinematical information for these stars, we conclude that the southern region closest to the LMC likely has an LMC origin, whereas that closest to the SMC has an SMC origin and the other two southern regions have a mix of LMC and SMC origins. Our results add to the evidence that the southern substructures of the LMC periphery are the product of close interactions between the LMC and SMC, and thus likely hold important clues that can constrain models of their detailed dynamical histories.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".