The Merging Galaxy Cluster Environment Affects the Morphology of Radio Active Galactic Nuclei
Bibliographic record
Abstract
Abstract It has previously been found that the galaxy cluster environment can affect the fueling and evolution of active galactic nuclei (AGN). This work examines the effect of the merging cluster environment on the properties of radio AGN by comparing the radio morphology of cluster members in a sample of four merging and eight relaxed galaxy clusters at low redshift (z < 0.2). Using 144 MHz data from the LOFAR Two-meter Sky Survey and Zooniverse, we classify the radio morphology of the radio-detected cluster members using the following morphology classes: compact, compact extended, extended, jetted, and disturbed. We find that the merging cluster environment has a statistically significant, higher population proportion of disturbed (bent and head tail) sources, indicating that the merging environment can affect the morphology of cluster radio AGN. We also investigate the number of AGN that are detected in the radio data only and the number that are detected in both the radio and optical data in mergers and nonmergers. We find that the merging cluster environment has a higher population proportion of AGN that are identified only as radio AGN compared to AGN that are identified as both radio and optical AGN. Overall, we find that the merging environment affects certain radio AGN (disturbed and only radio-identified AGN), but not all.
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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 | 0.000 |
| 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".