On the Origin of Quenched but Gas-rich Regions at Kiloparsec Scales in Nearby Galaxies
Bibliographic record
Abstract
Abstract We use resolved spectroscopy from MaNGA to investigate the significance of both local and global properties of galaxies to the cessation of star formation at kpc scales. Quenched regions are identified from a sample of isolated disk galaxies by a single-parameter criterion D n (4000) - log EW(Hα) > 1.6 − log 2 = 1.3 , and are divided into gas-rich quenched regions (GRQRs) and gas-poor quenched regions according to the surface density of cold gas (Σgas). Both types of quenched regions tend to be hosted by non-AGN galaxies with relatively high mass (M * ≳ 1010 M ⊙) and red colors (NUV − r ≳ 3), as well as low star formation rate and high central density at fixed mass. They span wide ranges in other properties including structural parameters that are similar to the parent sample, indicating that the conditions responsible for quenching in gas-rich regions are largely independent of the global properties of galaxies. We train random forest classifiers and regressors for predicting quenching in our sample with 15 local/global properties. Σ* is the most important property for quenching, especially for GRQRs. These results strongly indicate the important roles of low-mass hot evolved stars, which are numerous and long-lived in quenched regions and can provide substantial radiation pressure to support the surrounding gas against gravitational collapse. The different feature importance for quenching, as found previously by A. F. L. Bluck et al., is partly due to the different definitions of quenched regions, particularly the different requirements on EW(Hα).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".