Resolved Molecular Gas Observations of MaNGA Post-starbursts Reveal a Tumultuous Past
Bibliographic record
Abstract
Abstract Post-starburst (PSB) galaxies have recently and rapidly quenched their star formation; thus, they are an important way to understand how galaxies transition from star-forming late types to quiescent early types. The recent discovery of large cold gas reservoirs in PSB galaxies calls into question the theory that galaxies must lose their gas to become quiescent. Optical Integral Field Spectroscopy (IFS) surveys have revealed two classes of PSB galaxies: central PSB (cPSB) galaxies with central quenching regions and ring PSB (rPSB) galaxies with quenching in their outskirts. We analyze a sample of 13 nearby (z < 0.1) PSB galaxies with spatially resolved optical IFS data from the Mapping Nearby Galaxies at Apache Point Observatory survey and matched resolution Atacama Large Millimeter/submillimeter Array observations of 12CO(1–0). Disturbed stellar kinematics in 7/13 of our PSB galaxies and centrally concentrated molecular gas is consistent with a recent merger for most of our sample. In galaxies without merger evidence, alternate processes may funnel gas inward and suppress star formation, which may include outflows, stellar bars, and minor mergers or interactions. The star formation efficiencies of the PSB regions in nearly half our galaxies are suppressed while the gas fractions are consistent with star-forming galaxies. Active galactic nucleus (AGN) feedback may drive this stabilization, and we observe AGN-consistent emission in the centers of 5/13 galaxies. Finally, our cPSB and rPSB galaxies have similar properties except the ionized and molecular gas in cPSB galaxies is more disturbed. Overall, the molecular gas in our PSB galaxies tends to be compact and highly disturbed, resulting in concentrated gas reservoirs unable to form stars efficiently.
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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 | 0.000 |
| 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".