WISDOM Project – X. The morphology of the molecular ISM in galaxy centres and its dependence on galaxy structure
Bibliographic record
Abstract
ABSTRACT We use high-resolution maps of the molecular interstellar medium (ISM) in the centres of 86 nearby galaxies from the millimetre-Wave Interferometric Survey of Dark Object Masses (WISDOM) and Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) surveys to investigate the physical mechanisms setting the morphology of the ISM at molecular cloud scales. We show that early-type galaxies tend to have smooth, regular molecular gas morphologies, while the ISM in spiral galaxy bulges is much more asymmetric and clumpy when observed at the same spatial scales. We quantify these differences using non-parametric morphology measures (Asymmetry, Smoothness, and Gini), and compare these measurements with those extracted from idealized galaxy simulations. We show that the morphology of the molecular ISM changes systematically as a function of various large-scale galaxy parameters, including galaxy morphological type, stellar mass, stellar velocity dispersion, effective stellar mass surface density, molecular gas surface density, star formation efficiency, and the presence of a bar. We perform a statistical analysis to determine which of these correlated parameters best predicts the morphology of the ISM. We find the effective stellar mass surface (or volume) density to be the strongest predictor of the morphology of the molecular gas, while star formation and bars maybe be important secondary drivers. We find that gas self-gravity is not the dominant process shaping the morphology of the molecular gas in galaxy centres. Instead effects caused by the depth of the potential well, such as shear, suppression of stellar spiral density waves, and/or inflow, affect the ability of the gas to fragment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".