Galaxy mergers in <scp>eagle</scp> do not induce a significant amount of black hole growth yet do increase the rate of luminous AGN
Bibliographic record
Abstract
ABSTRACT We investigate the connection between galaxy–galaxy mergers and enhanced black hole (BH) growth using the cosmological hydrodynamical eagle simulation. We do this via three methods of analysis, investigating: the merger fraction of AGN, the AGN fraction of merging systems, and the AGN fraction of galaxies with close companions. In each case, we find an increased abundance of AGN within merging systems relative to control samples of inactive or isolated galaxies (by up to a factor of ≈3 depending on the analysis method used), confirming that mergers are enhancing BH accretion rates for at least a subset of the galaxy population. The greatest excess of AGN triggered via a merger are found in lower mass (M* ∼ 1010 M⊙) gas rich (fgas > 0.2) central galaxies with lower mass BHs (MBH ∼ 107 M⊙) at lower redshifts (z < 1). We find no enhancement of AGN triggered via mergers in more massive galaxies (M* ≳ 1011 M⊙). The enhancement of AGN is not uniform throughout the phases of a merger, and instead peaks within the early remnants of merging systems (typically lagging ≈300 Myr post-coalescence of the two galaxies at z = 0.5). We argue that neither major ($M_{\mathrm{*,1}} / M_{\mathrm{*,2}} \ge \frac{1}{4}$) nor minor mergers ($\frac{1}{10} \lt M_{\mathrm{*,1}} / M_{\mathrm{*,2}} \lt \frac{1}{4}$) are statistically relevant for enhancing BH masses globally. Whilst at all redshifts the galaxies experiencing a merger have accretion rates that are on average 2–3 times that of isolated galaxies, the majority of mass that is accreted on to BHs occurs outside the periods of a merger. We compute that on average no more than 15 per cent of a BHs final day mass comes from the enhanced accretion rates triggered via a merger.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".