The Kinematic Bimodality: Efficient Feedback and Cold Gas Deficiency in Slow-rotating Galaxies
Bibliographic record
Abstract
Abstract The bimodality in the stellar spin of low-redshift (massive) galaxies, ubiquitously existing at all star formation levels and in diverse environments, suggests that galaxies grow and quench through two diverged evolutionary pathways. For spheroid-dominated galaxies of slow stellar rotation, the age composition and metallicity of their stellar populations are evidence of a fast quenching history with significant gas outflows. In this work, we measure the spin parameter λ R e , i.e., the normalized specific angular momentum of stars, out of the MaNGA integral field spectroscopy for about 10,000 galaxies. Among the two-thirds with H i follow-up observations (z ≲ 0.05), we find that, compared to fast-rotating galaxies of the same stellar mass and star formation, the galaxy populations with slower rotation are generally more H i gas-poor, robust against further environmental restriction and with nondetections properly taken into account using the stacking technique. This cold gas deficit of slow-rotating galaxies is most apparent at high mass ∼ 10 11 ⊙ below the star formation main sequence, supporting the pivotal role of gas outflows in their quenching history. With hints from H i velocity distributions, we suspect that massive gas outflows among the slow-rotating population are facilitated by high ejective feedback efficiency, which is a result of extensive coupling between disturbed volume-filling cold gas and (commonly) biconical feedback from central black holes. By contrast, in fast-rotating disk galaxies the feedback energy mostly goes to the hot circumgalactic medium rather than directly impacting the dense and planar cold gas, thus making the feedback mainly preventive against further gas inflow.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".