Applying a star formation model calibrated on high-resolution interstellar medium simulations to cosmological simulations of galaxy formation
Bibliographic record
Abstract
ABSTRACT Modern high-resolution simulations of the interstellar medium (ISM) have shown that key factors in governing star formation are the competing influences of radiative dissipation, pressure support driven by stellar feedback, and gravity. However, cosmological simulations of galaxy formation are unable to resolve this physics in detail and therefore rely on approximate treatments, often taking the form of empirical subgrid models of the ISM expressed in terms of an effective equation of state (EOS). Here we seek to improve these heuristic models by directly fitting to results of the high-resolution TIGRESS simulations, which have shown that the dynamical equilibrium of the ISM can be understood in terms of a pressure-regulated feedback-modulated (PRFM) model for star formation. We explore a simple subgrid model that draws on the PRFM concept but uses only local quantities. It accurately reproduces the TIGRESS scalings for pure gas discs, but predicts slightly less star formation in the presence of an additional stellar disc. We compare this to the older Springel & Hernquist and TNG prescriptions by applying all three models to isolated simulations of disc galaxies as well as to a set of high-resolution zoom-in simulations carried out with a novel ‘multizoom’ technique that we introduce here. The softer TIGRESS EOS produces substantially thinner disc galaxies, which has important ramifications for disc stability and galaxy morphology. The stellar mass of galaxies is hardly modified at low redshift, reflecting the dominating influence of large-scale gaseous inflows and outflows, which are not sensitive to the EOS itself.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".