Interacting galaxies in the IllustrisTNG simulations − IV: enhanced supermassive black hole accretion rates in post-merger galaxies
Bibliographic record
Abstract
ABSTRACT We present an analysis of the instantaneous supermassive black hole (SMBH) accretion rates in a collection of 1563 post-merger galaxies drawn from the IllustrisTNG simulation. Our sample consists of galaxies that have experienced a merger in the last simulation snapshot (within ∼ 160 Myrs of coalescence) in the redshift range 0 < z < 1, with merger stellar mass ratios >1: 10 and post-merger stellar masses >1010M⊙. We find that, on average, the accretion rates of the post-mergers are ∼1.7 times higher than in a control sample and that post-mergers are three to four times more likely to experience a luminous active galactic nuclei (AGN) phase than isolated galaxies. SMBH accretion rate enhancements persist for ∼2 Gyrs after coalescence, significantly exceeding the ∼500 Myr lifetime of star formation rate enhancements. We find that the presence of simultaneous enhancements in both the star formation and SMBH accretion rates depends on both the mass ratio of the merger and on the gas mass of the post-merger galaxy. Despite these accretion rate enhancements, only ∼35 per cent of post-mergers experience a luminous AGN (Lbol > 1044erg/s) within 500 Myrs after coalescence, and fewer than 10 per cent achieve a luminosity in excess of Lbol > 1045erg/s. Moreover, only ∼10 per cent of the highest luminosity (Lbol > 1045erg/s) AGN in the IllustrisTNG galaxy sample are recent mergers. Our results are therefore consistent with a picture in which mergers can (but do not always) trigger AGN activity, but where the majority of galaxies hosting high luminosity AGN are not recent mergers.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".