Dwarf galaxy archaeology from chemical abundances and star-formation histories
Bibliographic record
Abstract
ABSTRACT We model the stellar abundances and ages of two disrupted dwarf galaxies in the Milky Way stellar halo: Gaia-Sausage Enceladus (GSE) and Wukong/LMS-1. Using a statistically robust likelihood function, we fit one-zone models of galactic chemical evolution with exponential infall histories to both systems, deriving e-folding time-scales of τin = 1.01 ± 0.13 Gyr for GSE and $\tau _\text{in} = 3.08^{+3.19}_{-1.16}$ Gyr for Wukong/LMS-1. GSE formed stars for $\tau _\text{tot} = 5.40^{+0.32}_{-0.31}$ Gyr, sustaining star formation for ∼1.5–2 Gyr after its first infall into the Milky Way ∼10 Gyr ago. Our fit suggests that star formation lasted for $\tau _\text{tot} = 3.36^{+0.55}_{-0.47}$ Gyr in Wukong/LMS-1, though our sample does not contain any age measurements. The differences in evolutionary parameters between the two are qualitatively consistent with trends with stellar mass M⋆ predicted by simulations and semi-analytic models of galaxy formation. Our inferred values of the outflow mass-loading factor reasonably match $\eta \propto M_\star ^{-1/3}$ as predicted by galactic wind models. Our fitting method is based only on Poisson sampling from an evolutionary track and requires no binning of the data. We demonstrate its accuracy by testing against mock data, showing that it accurately recovers the input model across a broad range of sample sizes (20 ≤ N ≤ 2000) and measurement uncertainties (0.01 ≤ σ[α/Fe], σ[Fe/H] ≤ 0.5; $0.02 \le \sigma _{\log _{10}(\text{age})} \le 1$). Due to the generic nature of our derivation, this likelihood function should be applicable to one-zone models of any parametrization and easily extensible to other astrophysical models which predict tracks in some observed space.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".