Metallicity Gradients in Modern Cosmological Simulations. I. Tension between Smooth Stellar Feedback Models and Observations
Bibliographic record
Abstract
Abstract The metallicity of galaxies, and its variation with galactocentric radius, provides key insights into the formation histories of galaxies and the physical processes driving their evolution. In this work, we analyze the radial metallicity gradients of star-forming galaxies in the EAGLE, Illustris, IllustrisTNG, and SIMBA cosmological simulations across broad mass (10 8.0 M ⊙ ≤ M ⋆ ≲ 10 12.0 M ⊙ ) and redshift (0 ≤ z ≤ 8) ranges. We find that all simulations predict strong negative (i.e., radially decreasing) metallicity gradients at early cosmic times, likely due to their similar treatments of relatively smooth stellar feedback not providing sufficient mixing to quickly flatten gradients. The strongest redshift evolution occurs in galaxies with stellar masses of 10 10.0 –10 11.0 M ⊙ , while galaxies with stellar mass < 10 10 M ⊙ and >10 11 M ⊙ exhibit weaker redshift evolution. Our result of negative gradients at high redshift contrast with the many positive and flat gradients in the 1 < z < 4 observational literature. At z > 6, the negative gradients observed with JWST and the Atacama Large Millimeter/submillimeter Array are flatter than those in simulations, albeit with closer agreement than at lower redshift. Overall, we suggest that these smooth stellar feedback galaxy simulations may not sufficiently mix their metal content radially, and that either stronger stellar feedback or additional subgrid turbulent metal diffusion models may be required to better reproduce observed metallicity gradients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".