<scp>Aeos</scp>: The Impact of Population III Initial Mass Function and Star-by-star Models in Galaxy Simulations
Bibliographic record
Abstract
Abstract We explore the effect of variations in the Population III initial mass function (IMF) and star-by-star feedback on early galaxy formation and evolution using the Aeos simulations. We compare simulations with two different Population III IMFs: M char = 10 M ⊙ and M max = 100 M ⊙ (Aeos10) and M char = 20 M ⊙ and M max = 300 M ⊙ (Aeos20). Aeos20 produces significantly more ionizing photons, ionizing 30% of the simulation volume by z ≈ 14, compared to 9% in Aeos10. This enhanced ionization suppresses galaxy formation on the smallest scales. Differences in Population III IMF also affect chemical enrichment. Aeos20 produces Population II stars with higher abundances, relative to iron, of light and α-elements, a stronger odd–even effect, and a higher frequency of carbon-enhanced metal-poor stars. The abundance scatter between different Population II galaxies dominates the differences due to Population III IMF, though, implying a need for a larger sample of Population II stars to interpret the impact of Population III IMF on early chemical evolution. We also compare the Aeos simulations to traditional simulations that use single stellar population particles. We find that star-by-star modeling produces a steeper mass–metallicity relation due to less bursty feedback. These results highlight the strong influence of the Population III IMF on early galaxy formation and chemical evolution, emphasizing the need to account for IMF uncertainties in simulations and the importance of metal-poor Population II stellar chemical abundances when studying the first stars.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".