THE LOCAL ENVIRONMENTS OF INTERACTING GALAXY SYSTEMS
Bibliographic record
Abstract
Gravitational interactions between galaxies are widely believed to be the principal mechanism responsible for triggering non-thermal activity in galactic nuclei. We investigate the connection between interacting galaxies and active galactic nuclei (AGNs) in the local universe by comparing the clustering properties of their environments on 0.5 Mpc scales, as quantified by the amplitude of the spatial cross-correlation function. If a direct evolutionary relationship exists, the samples should be situated in environments that are statistically similar. It was previously found that a sample of 33 Seyfert galaxies with z ⩽ 0.05 is located in environments comparable to those of isolated field galaxies. The analysis presented here reveals that a well-matched sample of 52 strongly interacting galaxy systems are preferentially situated in regions more consistent with an Abell Richness Class between 0 and 1. The apparent dissimilarity in the environments thus provides a strong argument against a causal link between major galaxy interactions and Seyfert activity. In contrast, we find that the environments of luminous quasars with z ⩽ 1 exhibit a range of richness levels that are more consistent with the interacting galaxies, suggesting that these objects could be triggered by interactions. Together the results presented here indicate that the relative importance of different mechanisms in initiating nuclear activity may vary with AGN luminosity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".