The ALMaQUEST Survey XI: a strong but non-linear relationship between star formation and dynamical equilibrium pressure
Bibliographic record
Abstract
ABSTRACT We present the extended ALMA MaNGA QUEnching and STar formation survey (ALMaQUEST), a combination of the original 46 ALMaQUEST galaxies plus new ALMA observations for a further 20 interacting galaxies. Three well-studied scaling relations are fit to the 19 999 star-forming spaxels in the extended sample, namely the resolved Schmidt–Kennicutt relation, the resolved star-forming main-sequence and the resolved molecular gas main sequence. We additionally investigate the relationship between the dynamical equilibrium pressure (PDE) and star formation rate surface density (ΣSFR), which we refer to as the resolved PDE (rPDE) relation. Contrary to previous studies that have focussed on normal star-forming galaxies and found an approximately linear rPDE relation, the presence of more vigourously star-forming galaxies in the extended ALMaQUEST sample reveals a marked turnover in the relation at high pressures. Although the scatter around the linear fit to the rPDE relation is similar to the other three relations, a random forest analysis, which can extract non-linear dependences, finds that PDEis unambiguously more important than either $\Sigma _{\rm H_2}$ or Σ⋆ for predicting ΣSFR. We compare the observed rPDE relation to the prediction of the pressure-regulated feedback-modulated (PRFM) model of star formation, finding that galaxies residing on the global SFMS do indeed closely follow the rPDE relation predicted by the PRFM theory. However, galaxies above and below the global SFMS show significant deviations from the model. Galaxies with high SFR are instead consistent with models that include other contributions to turbulence in addition to the local star formation feedback.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".