Interplay of stellar and gas-phase metallicities: unveiling insights for stellar feedback modelling with Illustris, IllustrisTNG, and EAGLE
Bibliographic record
Abstract
ABSTRACT The metal content of galaxies provides a window into their formation in the full context of the cosmic baryon cycle. In this study, we examine the relationship between stellar mass and stellar metallicity (MZ*R) in the hydrodynamic simulations Illustris, TNG, and EAGLE (Evolution and Assembly of GaLaxies and their Environment) to understand the global properties of stellar metallicities within the feedback paradigm employed by these simulations. Interestingly, we observe significant variations in the overall normalization and redshift evolution of the MZ*R across the three simulations. However, all simulations consistently demonstrate a tertiary dependence on the specific star formation rate (sSFR) of galaxies. This finding parallels the relationship seen in both simulations and observations between stellar mass, gas-phase metallicity, and some proxy of galaxy gas content (e.g. SFR, gas fraction, and atomic gas mass). Since we find this correlation exists in all three simulations, each employing a subgrid treatment of the dense, star-forming interstellar medium (ISM) to simulate smooth stellar feedback, we interpret this result as a fairly general feature of simulations of this kind. Furthermore, with a toy analytic model, we propose that the tertiary correlation in the stellar component is sensitive to the extent of the ‘burstiness’ of feedback within galaxies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".