The star formation history of the Sagittarius stream
Bibliographic record
Abstract
We present the first detailed quantitative study of the stellar populations of the Sagittarius (Sgr) streams within the Stripe 82 region, using photometric and spectroscopic observations from the Sloan Digital Sky Survey (SDSS). The star formation history (SFH) is determined separately for the bright and faint Sgr streams, to establish whether both components consist of a similar stellar population mix or have a distinct origin. Best-fitting SFH solutions are characterized by a well-defined, tight sequence in age–metallicity space, indicating that star formation occurred within a well-mixed, homogeneously enriched medium. Star formation rates dropped sharply at an age of ≈5–7 Gyr, possibly related to the accretion of Sgr by the MW. Finally, the Sgr sequence displays a change of slope in age–metallicity space at an age between 11 and 13 Gyr consistent with the Sgr α-element knee, indicating that supernovae Type Ia started contributing to the abundance pattern ≈1–3 Gyr after the start of star formation. Results for both streams are consistent with being drawn from the parent Sgr population mix, but at different epochs. The SFH of the bright stream starts from old, metal-poor populations and extends to a metallicity of [Fe/H] ≈ −0.7, with peaks at ≈7 and 11 Gyr. The faint SFH samples the older, more metal-poor part of the Sgr sequence, with a peak at ancient ages and stars mostly with [Fe/H] < −1.3 and age > 9 Gyr. Therefore, we argue in favour of a scenario where the faint stream consists of material stripped (i) earlier, and (ii) from the outskirts of the Sgr dwarf.
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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 | 0.000 |
| 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".