Main-sequence Turnoff Stars as Probes of the Ancient Galactic Relic: Chemo-dynamical Analysis of a Pilot Sample
Bibliographic record
Abstract
Abstract The main-sequence turnoff (MSTO) stars well preserve the chemical properties where they were born, making them ideal tracers for studying the stellar population. We perform a detailed chemo-dynamical analysis on moderately metal-poor (−2.0 < [Fe/H] < −1.0) MSTO stars to explore the early accretion history of the Milky Way. Our sample includes four stars observed with high-resolution spectroscopy using ESPaDOnS at the Canada–France–Hawaii Telescope and 163 nearby MSTO stars selected from the SAGA database with high-resolution results. Within the action-angle spaces, we identified Gaia–Sausage–Enceladus (GSE, 35 objects), stars born in the Milky Way (in situ, 31 objects), and other substructures (21 objects). We find that both GSE and in situ stars present a similar Li plateau around A(Li) ∼ 2.17. GSE shows a clear α-knee feature in Mg at [Fe/H] ∼ −1.60 ± 0.06, while the α-elements of in situ stars remain nearly constant within this metallicity range. The iron-peak elements show little difference between GSE and in situ stars except for Zn and Ni, which decrease in GSE at [Fe/H] > −1.6, while they remain constant for in situ stars. Among heavy elements, GSE shows overall enhancement in Eu, with [Ba/Eu] increasing with the metallicity, while this ratio remains almost constant for in situ stars, suggesting the contribution of longer timescale sources to the s-process in GSE. Moreover, for the first time, we present the r-process abundance pattern for an extremely r-process enhanced (r-II) GSE star, which appears consistent with the solar r-process pattern except for Pr. Further investigation of larger GSE samples using high-resolution spectra is required to explore the reason for the significantly higher Pr in the GSE r-II star.
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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 | 0.000 | 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".