Filamentary Hierarchies and Superbubbles. I. Characterizing Filament Properties across a Simulated Spiral Galaxy
Bibliographic record
Abstract
Abstract High-resolution surveys reveal that the interstellar medium in the Milky Way and nearby galaxies consists of interlinked hierarchies of filamentary structure and superbubbles extending from galactic to subparsec scales. The characterization of filament properties across this hierarchy is of fundamental importance for the origin of giant molecular clouds and their star clusters. In this paper, we characterize the properties of filaments greater than 25 pc in length that are produced in the multiscale galactic MHD simulations of B. Zhao et al. By adapting the FilFinder algorithm of E. W. Koch & E. W. Rosolowsky, we extract over 500 filaments ranging up to 10 kpc in scale, to derive the probability distribution functions for filament masses and lengths, magnetic field orientations, and the gravitational stability and fragmentation patterns of filaments. We find power-law distributions for filament masses and lengths. The former has a power-law index α m = 1.85 that is nearly identical to that of observed giant molecular cloud (GMC) mass functions in extragalactic and Galactic surveys, suggesting that GMC properties are inherited from their host filaments. The fragmentation of magnetized filaments on 200 pc scales or less occurs when they exceed an average critical line mass, as predicted by theory. On larger scales, however, kiloparsec filaments form out of the cold neutral medium, and fragmentation follows local variations in the critical line mass along spiral arms or at the boundaries of superbubbles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".