Testing Feedback-regulated Star Formation in Gas-rich, Turbulent Disk Galaxies
Bibliographic record
Abstract
In this paper we compare the molecular gas depletion times and midplane hydrostatic pressure in turbulent, star-forming disk galaxies to internal properties of these galaxies. For this analysis we use 17 galaxies from the DYNAMO sample of nearby (z ∼ 0.1) turbulent disks. We find a strong correlation, such that galaxies with lower molecular gas depletion time (t dep) have higher gas velocity dispersion (σ). Within the scatter of our data, our observations are consistent with the prediction that made in theories of feedback-regulated star formation. We also show a strong, single power-law correlation between midplane pressure (P) and star formation rate surface density (ΣSFR), which extends for 6 orders of magnitude in pressure. Disk galaxies with lower pressure are found to be roughly in agreement with theoretical predictions. However, in galaxies with high pressure we find P/ΣSFR values that are significantly larger than theoretical predictions. Our observations could be explained with any of the following: (1) the correlation of ΣSFR-P is significantly sublinear; (2) the momentum injected from star formation feedback (p ∗/m ∗) is not a single, universal value; or (3) alternate sources of pressure support are important in gas-rich disk galaxies. Finally, using published survey results, we find that our results are consistent with the cosmic evolution of t dep(z) and σ(z). Our interpretation of these results is that the cosmic evolution of t dep may be regulated not just by the supply of gas but also by the internal regulation of star formation via feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".