The Effect of Minor and Major Mergers on the Evolution of Low-excitation Radio Galaxies
Bibliographic record
Abstract
Abstract We use deep, μ r ≲ 28 mag arcsec−2, r-band imaging from the Dark Energy Camera Legacy Survey to search for past, or ongoing, merger activity in a sample of 282 low-excitation radio galaxies (LERGs) at z < 0.07. Our principal aim is to assess the the role of mergers in the evolution of LERGs. Exploiting the imaging depth, we classify tidal remnants around galaxies as both minor and major morphological disturbances for our LERG sample and 1622 control galaxies matched in redshift, stellar mass, and environment. In groups and in the field, the LERG minor merger fraction is consistent with the control population. In galaxy clusters, 8.8 ± 2.9% of LERGs show evidence of recent minor mergers in contrast to 23.0 ± 2.0% of controls. This ∼4σ deficit of minor mergers in cluster LERGs suggests these events may inhibit this type of nuclear activity for galaxies within the cluster environment. We observe a >4σ excess of major mergers in the LERGs with M * ≲ 1011 M⊙, with 10 ± 1.5% of these active galactic nuclei involved in such large-scale interactions compared to 3.2 ± 0.4% of control galaxies. This excess of major mergers in LERGs decreases with increasing stellar mass, vanishing by M * > 1011.3 M⊙. These observations show that minor mergers do not fuel LERGs, and are consistent with typical LERGs being powered by accretion of matter from their halo. Where LERGs are associated with major mergers, these objects may evolve into more efficiently accreting active galactic nuclei as the merger progresses and more gas falls on to the central engine.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".