Selecting accreted populations: metallicity, elemental abundances, and ages of the <i>Gaia</i>-Sausage-Enceladus and Sequoia populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Identifying stars found in the Milky Way as having formed in situ or accreted can be a complex and uncertain undertaking. We use Gaia kinematics and APOGEE elemental abundances to select stars belonging to the Gaia-Sausage-Enceladus (GSE) and Sequoia accretion events. These samples are used to characterize the GSE and Sequoia population metallicity distribution functions, elemental abundance patterns, age distributions, and progenitor masses. We find that the GSE population has a mean [Fe/H] ∼ −1.15 and a mean age of 10–12 Gyr. GSE has a single sequence in [Mg/Fe] versus [Fe/H] consistent with the onset of SN Ia Fe contributions and uniformly low [Al/Fe] of ∼−0.25 dex. The derived properties of the Sequoia population are strongly dependent on the kinematic selection. We argue the selection with the least contamination is Jϕ/Jtot &lt; −0.6 and (Jz − JR)/Jtot &lt; 0.1. This results in a mean [Fe/H] ∼ −1.3 and a mean age of 12–14 Gyr. The Sequoia population has a complex elemental abundance distribution with mainly high-[Mg/Fe] stars. We use the GSE [Al/Fe] versus [Mg/H] abundance distribution to inform a chemically based selection of accreted stars, which is used to remove possible contaminant stars from the GSE and Sequoia samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it