On the slow quenching of ℳ* galaxies: heavily obscured AGNs clarify the picture
Bibliographic record
Abstract
ABSTRACT We investigate the connection between X-ray and radio-loud active galactic nuclei (AGNs) and the physical properties of their evolved and massive host galaxies, focussing on the mass-related quenching channel followed by $\mathcal {M}^\star (\simeq 10^{10.6} \, \mathrm{M}_\odot)$ galaxies in the rest-frame NUV–r versus r–K (NUVrK) colour diagram at 0.2 < z < 0.5. While our results confirm that (1) radio-loud AGNs are predominantly hosted by already-quenched and very massive ($M_*\gt 10^{11}\, \mathrm{M}_\odot$) galaxies, ruling out their feedback as a primary driver of $\mathcal {M}^\star$ galaxy quenching, we found that (2) X-ray AGNs affected by heavy obscuration of their soft X-ray emission are mostly hosted by $\mathcal {M}^\star$ galaxies that are in the process of quenching. This is consistent with a quenching scenario that involves mergers of (gas-poor) $\mathcal {M}^\star$ galaxies after the onset of the quenching process, i.e. a scenario where $\mathcal {M}^\star$ galaxy mergers are not the cause but rather an aftermath of the quenching mechanism(s). In that respect, we discuss how our results may support a picture where the slow quenching of $\mathcal {M}^\star$ galaxies happens due to halo–halo mergers along cosmic filaments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".