The stellar metallicity distribution of disc galaxies and bulges in cosmological simulations
Bibliographic record
Abstract
By means of high-resolution cosmological hydrodynamical simulations of Milky Way (MW) like disc galaxies, we conduct an analysis of the associated stellar metallicity distribution functions (MDFs). After undertaking a kinematic decomposition of each simulation into spheroid and disc subcomponents, we compare the predicted MDFs to those observed in the solar neighbourhood and the Galactic bulge. The effects of the star formation density threshold are visible in the star formation histories, which show a modulation in their behaviour driven by the threshold. The derived MDFs show median metallicities lower by 0.2–0.3 dex than the MDF observed locally in the disc and in the Galactic bulge. Possible reasons for this apparent discrepancy include the use of low stellar yields and/or centrally concentrated star formation. The dispersions are larger than the one of the observed MDF; this could be due to simulated discs being kinematically hotter relative to the MW. The fraction of low-metallicity stars is largely overestimated, visible from the more negatively skewed MDF with respect to the observational sample. For our fiducial MW analogue, we study the metallicity distribution of the stars born in situ relative to those formed via accretion (from disrupted satellites), and demonstrate that this low-metallicity tail to the MDF is populated primarily by accreted stars. Enhanced supernova and stellar radiation energy feedback to the surrounding interstellar media of these pre-disrupted satellites is suggested as an important regulator of the MDF skewness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".