The Origin and Evolution of the Mass-Metallicity Relationship for Galaxies: Results from Cosmological <i>N</i> -Body Simulations
Bibliographic record
Abstract
We examine the origin and evolution of the mass-metallicity relationship (MZR, M * - Z ) for galaxies using high-resolution cosmological smoothed particle hydrodynamics (SPH) + N -body simulations that include a physically motivated description of supernova feedback and subsequent metal enrichment. We discriminate between two sources that may contribute to the origin of the MZR: (1) metal and baryon loss due to gas outflow or (2) inefficient star formation at the lowest galaxy masses. Our simulated galaxies reproduce the observed MZR in shape and normalization at both z = 0 and 2. We find that baryon loss occurs due to UV heating before star formation turns on in galaxies with M bar < 10 8 M ☉ , but that some gas loss due to supernova-induced winds is required to subsequently reproduce the low effective chemical yield observed in low-mass galaxies. Despite this, we show that low star formation efficiencies, regulated by supernova feedback, are primarily responsible for the lower metallicities of low-mass galaxies and the overall M * - Z trend. We find that the shape of the MZR is relatively constant with redshift but that its normalization increases with time. Simulations with no energy feedback from supernovae overproduce metals at low galaxy masses by rapidly transforming a large fraction of their gas into stars. Despite the fact that our low-mass galaxies have lost a majority of their baryons, they are still the most gas-rich objects in our simulations due to their low star formation efficiencies.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".