The interconnection between galaxy mergers, AGN activity, and rapid quenching of star formation in simulated post-merger galaxies
Bibliographic record
Abstract
ABSTRACT We investigate the role of galaxy mergers on supermassive black hole (SMBH) accretion and star formation quenching in three state-of-the-art cosmological simulations with contrasting physics models: EAGLE, Illustris, and IllustrisTNG (TNG). We find that recently coalesced ‘post-mergers’ in all three simulations have elevated SMBH accretion rates by factors of ∼2–5. However, rapid (within 500 Myr of coalescence) quenching of star formation is rare, with incidence rates of $0.4{{\ \rm per\ cent}}$ in Illustris, $4.5{{\ \rm per\ cent}}$ in EAGLE, and $10{{\ \rm per\ cent}}$ in TNG. The rarity of quenching in post-mergers results from substantial gas reservoirs that remain intact after the merger. The post-mergers that do successfully quench tend to be those that had both low pre-merger gas fractions as well as those that experience the largest gas losses. Although rare, the recently quenched fraction of post-mergers is still elevated compared to a control sample of non-mergers by factors of two in TNG and 11 in EAGLE. Conversely, quenching is rarer in Illustris post-mergers than in their control. Recent observational results by Ellison et al. have found rapid quenching to be at least 30 times more common in post-mergers, a significantly higher excess than found in any of the simulations. Our results therefore indicate that whilst merger-induced SMBH accretion is a widespread prediction of the simulation, its link to quenching depends sensitively on the physics models, and that none of the subgrid models of the simulations studied here can fully capture the connection between mergers and rapid quenching seen in observations.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".