An observational test for star formation prescriptions in cosmological hydrodynamical simulations
Bibliographic record
Abstract
State-of-the-art cosmological hydrodynamical simulations of galaxy formation have reached the point at which their outcomes result in galaxies with ever more realism. Still, the employed sub-grid models include several free parameters such as the density threshold, n, to localize the star-forming gas. In this work, we investigate the possibilities to utilize the observed clustered nature of star formation (SF) in order to refine SF prescriptions and constrain the density threshold parameter. To this end, we measure the clustering strength, correlation length, and power-law index of the two-point correlation function of young (τ < 50 Myr) stellar particles and compare our results to observations from the HST Legacy Extragalactic UV Survey (LEGUS). Our simulations reveal a clear trend of larger clustering signal and power-law index and lower correlation length as the SF threshold increases with only mild dependence on galaxy properties such as stellar mass or specific star formation rate. In conclusion, we find that the observed clustering of SF is inconsistent with a low threshold for SF (n < 1 cm−3) and strongly favours a high value for the density threshold of SF (n > 10 cm−3), as, for example employed in the NIHAO project.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".