STAR FORMATION AND FEEDBACK IN SMOOTHED PARTICLE HYDRODYNAMIC SIMULATIONS. II. RESOLUTION EFFECTS
Bibliographic record
Abstract
We examine the effect of mass and force resolution on a specific star formation (SF) recipe using a set of N -body/smooth particle hydrodynamic simulations of isolated galaxies. Our simulations span halo masses from 10 9 to 10 13 M ☉ , more than 4 orders of magnitude in mass resolution, and 2 orders of magnitude in the gravitational softening length, , representing the force resolution. We examine the total global SF rate, the SF history, and the quantity of stellar feedback and compare the disk structure of the galaxies. Based on our analysis, we recommend using at least 10 4 particles each for the dark matter (DM) and gas component and a force resolution of ≈ 10 −3 R vir when studying global SF and feedback. When the spatial distribution of stars is important, the number of gas and DM particles must be increased to at least 10 5 of each. Low-mass resolution simulations with fixed softening lengths show particularly weak stellar disks due to two-body heating. While decreasing spatial resolution in low-mass resolution simulations limits two-body effects, density and potential gradients cannot be sustained. Regardless of the softening, low-mass resolution simulations contain fewer high density regions where SF may occur. Galaxies of approximately 10 10 M ☉ display unique sensitivity to both mass and force resolution. This mass of galaxy has a shallow potential and is on the verge of forming a disk. The combination of these factors gives this galaxy the potential for strong gas outflows driven by supernova feedback and makes it particularly sensitive to any changes to the simulation parameters.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".