The role of feedback in shaping the structure of the interstellar medium
Bibliographic record
Abstract
We present an analysis of the role of feedback in shaping the neutral hydrogen (H i) content of simulated disc galaxies. For our analysis, we have used two realizations of two separate Milky Way-like (∼L⋆) discs – one employing a conservative feedback scheme (McMaster Unbiased Galaxy Survey), the other significantly more energetic [Making Galaxies In a Cosmological Context (MaGICC)]. To quantify the impact of these schemes, we generate zeroth moment (surface density) maps of the inferred H i distribution; construct power spectra associated with the underlying structure of the simulated cold interstellar medium, in addition to their radial surface density and velocity dispersion profiles. Our results are compared with a parallel, self-consistent, analysis of empirical data from The H i Nearby Galaxy Survey (THINGS). Single power-law fits (P ∝ kγ) to the power spectra of the stronger feedback (MaGICC) runs (over spatial scales corresponding to ∼0.5 to ∼20 kpc) result in slopes consistent with those seen in the THINGS sample (γ ∼ −2.5). The weaker feedback (MUGS) runs exhibit shallower power-law slopes (γ ∼ −1.2). The power spectra of the MaGICC simulations are more consistent though with a two-component fit, with a flatter distribution of power on larger scales (i.e. γ ∼ −1.4 for scales in excess of ∼2 kpc) and a steeper slope on scales below ∼1 kpc (γ ∼ −5), qualitatively consistent with empirical claims, as well as our earlier work on dwarf discs. The radial H i surface density profiles of the MaGICC discs show a clear exponential behaviour, while those of the MUGS suite are essentially flat; both behaviours are encountered in nature, although the THINGS sample is more consistent with our stronger (MaGICC) feedback runs.
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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 | 0.003 |
| 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 | 0.000 |
| 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 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".