Osaka Feedback Model. II. Modeling Supernova Feedback Based on High-resolution Simulations
Bibliographic record
Abstract
Abstract Feedback from supernovae (SNe) is an essential mechanism that self-regulates the growth of galaxies, and a better model of SN feedback is still needed in galaxy-formation simulations. In the first part of this paper, using an Eulerian hydrodynamic code Athena++, we find the universal scaling relations for the time evolution of momentum and radius for a superbubble, when the momentum and time are scaled by those at the shell-formation time. In the second part of this paper, we develop a SN feedback model based on the Athena++ simulation results utilizing Voronoi tessellation around each star particle, and implement it into the GADGET3-Osaka smoothed particle hydrodynamic code. Our feedback model was demonstrated to be isotropic and conservative in terms of energy and momentum. We examined the mass/energy/metal loading factors and find that our stochastic thermal feedback model produced galactic outflow that carries metals high above the galactic plane but with weak suppression of star formation. Additional mechanical feedback further suppressed star formation and brought the simulation results into better agreement with the observations of the Kennicutt–Schmidt relation, with all the results being within the uncertainties of observed data. We argue that both thermal and mechanical feedback are necessary for the SN feedback model of galaxy evolution when an individual SN bubble is unresolved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".