Mergers trigger active galactic nuclei out to <i>z</i> ∼ 0.6
Bibliographic record
Abstract
Aims. The fueling and feedback of active galactic nuclei (AGNs) are important for understanding the co-evolution between black holes and host galaxies. Mergers are thought to have the capability to bring gas inward and ignite nuclear activity, especially for more powerful AGNs. However, there is still significant ongoing debate on whether mergers can trigger AGNs and, if they do, whether mergers are a significant triggering mechanism. Methods. We selected a low-redshift (0.005 < z < 0.1) sample from the Sloan Digital Sky Survey and a high-redshift (0 < z < 0.6) sample from the Galaxy And Mass Assembly survey. We took advantage of the convolutional neural network technique to identify mergers. We used mid-infrared (MIR) color cut and optical emission line diagnostics to classify AGNs. We also included low excitation radio galaxies (LERGs) to investigate the connection between mergers and low accretion rate AGNs. Results. We find that AGNs are more likely to be found in mergers than non-mergers, with an AGN excess up to 1.81 ± 0.16, suggesting that mergers can trigger AGNs. We also find that the fraction of mergers in AGNs is higher than that in non-AGN controls, for both MIR and optically selected AGNs, as well as LERGs, with values between 16.40 ± 0.5% and 39.23 ± 2.10%, implying a non-negligible to potentially significant role of mergers in triggering AGNs. This merger fraction in AGNs increases as stellar mass increases, which supports the idea that mergers are more important for triggering AGNs in more massive galaxies. In terms of merger fraction as a function of AGN power we find a positive trend for MIR selected AGNs and a complex trend for optically selected AGNs, which we interpret under an evolutionary scenario proposed by previous studies. In addition, obscured MIR selected AGNs are more likely to be hosted in mergers than unobscured MIR selected AGNs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".