Gastro Library. I. The Simulated Chemodynamical Properties of Several Gaia–Sausage–Enceladus-like Stellar Halos
Bibliographic record
Abstract
Abstract The Milky Way (MW) stellar halo contains relics of ancient mergers that tell the story of our galaxy’s formation. Some of them are identified due to their similarity in energy, actions, and chemistry, referred to as the “chemodynamical space,” and are often attributed to distinct merger events. It is also known that our galaxy went through a significant merger event that shaped the local stellar halo during its first billion years. Previous studies using N-body only and cosmological hydrodynamical simulations have shown that such a single massive merger can produce several “signatures” in the chemodynamical space, which can potentially be misinterpreted as distinct merger events. Motivated by these, in this work we use a subset of the GASTRO library, which consists of several smoothed particle hydrodynamics+N-body models of a single accretion event in a MW-like galaxy. Here, we study models with orbital properties similar to the main merger event of our galaxy and explore the implications to known stellar halo substructures. We find that (i) supernova feedback efficiency influences the satellite’s structure and orbital evolution, resulting in distinct chemodynamical features for models with the same initial conditions; (ii) very retrograde high-energy stars are the most metal-poor of the accreted dwarf galaxy and could be misinterpreted as a distinct merger; (iii) the most bound stars are more metal-rich in our models, the opposite of what is observed in the MW, suggesting a secondary massive merger; and, finally, (iv) our models can reconcile other known apparently distinct substructures to a unique progenitor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".