Implementing Feedback in Simulations of Galaxy Formation: A Survey of Methods
Bibliographic record
Abstract
We present a detailed investigation of a number of different approaches to modelling feedback in simulations of galaxy formation. Gas-dynamic forces are evaluated using Smoothed Particle Hydrodynamics (SPH). Star formation and supernova feedback are included using a three parameter model which determines the star formation rate (SFR) normalization, feedback energy and lifetime of feedback regions. The star formation rate is calculated for all gas particles which fall within prescribed temperature, density and convergent flow criteria, and for cosmological simulations we also include a self-gravity criterion for gas particles to prevent star formation at high redshifts. A Lagrangian Schmidt law is used to calculate the star formation rate from the SPH density. Conversion of gas to stars is performed when the star mass for a gas particle exceeds a certain limit, typically half that of the gas particle. Feedback is incorporated by returning a precalculated amount of energy to the ISM as thermal heating. We compare the effects of distributing this energy over the smoothing scale or depositing it on a single particle. Radiative losses are prevented from heated particles by adjusting the density used in radiative cooling so that the decay of energy occurs over a set half-life, or by turning off cooling completely and allowing feedback regions a brief period of adiabatic expansion. We test the models on the formation of galaxies from cosmological
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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 | 0.001 | 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) | 0.001 | 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 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".