The distinct stellar metallicity populations of simulated Local Group dwarfs
Bibliographic record
Abstract
ABSTRACT A number of Local Group dwarf galaxies are known to have two spatially segregated stellar metallicity populations, a centrally concentrated metal-rich population, and a more extended metal-poor population. In this work we discuss mechanisms that lead to the formation of two spatially segregated metallicity populations. Using a set of high-resolution hydrodynamical simulations of Local Group-like environments, we select a sample of satellite and field galaxies, spanning the stellar mass range 106–109 M⊙, that exhibit bimodality in their metallicity distributions. Among those, we identify a subsample with a strong spatial segregation in the two populations. We find three distinct mechanisms for their formation. In field dwarfs and in a small fraction of satellites, a merger causes the metal-poor stars to migrate to larger radii and encourages the available gas to sink to the centre of the dwarf. Most of the gas is subsequently blown out of the halo through star formation feedback, but the remaining gas is consumed in the formation of a metal-rich population. In the exclusive case of satellites that have retained some of their gas at infall, it is the compression of this gas by ram pressure near pericentre that triggers the formation of metal-rich stars, whilst simultaneously preventing star formation at larger radii through stripping. Additionally, in a small number of field and satellite dwarfs, interactions with gaseous filaments and other galaxies can result in the formation of a metal-rich population. Regardless of the formation mechanism, a history of mergers typically enhances the spatial segregation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".