Suppressing star formation in quiescent galaxies with supermassive black hole winds
Bibliographic record
Abstract
Quiescent galaxies with little or no ongoing star formation dominate the galaxy population above $M_{*}\sim 2 \times 10^{10}~M_{\odot}$, where their numbers have increased by a factor of $\sim25$ since $z\sim2$. Once star formation is initially shut down, perhaps during the quasar phase of rapid accretion onto a supermassive black hole, an unknown mechanism must remove or heat subsequently accreted gas from stellar mass loss or mergers that would otherwise cool to form stars. Energy output from a black hole accreting at a low rate has been proposed, but observational evidence for this in the form of expanding hot gas shells is indirect and limited to radio galaxies at the centers of clusters, which are too rare to explain the vast majority of the quiescent population. Here we report bisymmetric emission features co-aligned with strong ionized gas velocity gradients from which we infer the presence of centrally-driven winds in typical quiescent galaxies that host low-luminosity active nuclei. These galaxies are surprisingly common, accounting for as much as $10\%$ of the population at $M_* \sim 2 \times 10^{10}~ M_{\odot}$. In a prototypical example, we calculate that the energy input from the galaxy's low-level active nucleus is capable of driving the observed wind, which contains sufficient mechanical energy to heat ambient, cooler gas (also detected) and thereby suppress star formation.
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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 | 0.000 | 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".