Bibliographic record
Abstract
ABSTRACT We use high-resolution magnetohydrodynamic simulations of isolated disc galaxies to investigate the co-evolution of magnetic fields with a self-regulated, star-forming interstellar medium (ISM). The simulations are conducted using the ramses adaptive mesh refinement code on the standard agora initial condition, with gas cooling, star formation, and feedback. We run galaxies with a variety of initial magnetic field strengths. The fields evolve and achieve approximate saturation within 500 Myr, but at different levels. The galaxies reach a quasi-steady state, with slowly declining star formation due to both gas consumption and increase in the field strength at intermediate ISM densities. We connect this behaviour to differences in the gas properties and overall structure of the galaxies. Stronger magnetic fields limit supernova bubble sizes. Different cases support the ISM using varying combinations of magnetic pressure, turbulence, and thermal energy. Initially, $\gtrsim\!\! 1\ \mu \mathrm{ G}$ magnetic fields evolve modestly and dominate support at all radii. Conversely, initially weaker fields grow through feedback and turbulence but never dominate the support. This is reflected in the stability of the gas disc. This interplay determines the overall distribution of star formation in each case. We conclude that an initially weak field can grow to produce a realistic model of a local disc galaxy, but starting with typically assumed field strengths ($\gtrsim\!\! 1\ \mu \mathrm{ G}$) will not.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".