Galaxy pairs in the Sloan Digital Sky Survey – XII. The fuelling mechanism of low-excitation radio-loud AGN
Bibliographic record
Abstract
Abstract We investigate whether the fuelling of low-excitation radio galaxies (LERGs) is linked to major galaxy interactions. Our study utilizes a sample of 10 800 spectroscopic galaxy pairs and 97 post-mergers selected from the Sloan Digital Sky Survey with matches to multiwavelength data sets. The LERG fraction amongst interacting galaxies is a factor of 3.5 higher than that of a control sample matched in local galaxy density, redshift and stellar mass. However, the LERG excess in pairs does not depend on projected separation and remains elevated out to at least 500 $h_{70}^{-1}$ kpc, suggesting that major mergers are not their main fuelling channel. In order to identify the primary fuelling mechanism of LERGs, we compile samples of control galaxies that are matched in various host galaxy and environmental properties. The LERG excess is reduced, but not completely removed, when halo mass or D4000 are included in the matching parameters. However, when bothMhalo and D4000 are matched, there is no LERG excess and the 1.4 GHz luminosities (which trace jet mechanical power) are consistent between the pairs and control. In contrast, the excess of optical and mid-IR selected active galactic nuclei (AGN) in galaxy pairs is unchanged when the additional matching parameters are implemented. Our results suggest that whilst major interactions may trigger optically and mid-IR selected AGN, the gas which fuels the LERGs has two secular origins: one associated with the large-scale environment, such as accretion from the surrounding medium or minor mergers, plus an internal stellar mechanism, such as winds from evolved stars.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".