The large-scale environment of 3CR radio galaxies at
Bibliographic record
Abstract
The question of whether and how the properties of radio galaxies (RGs) are connected with the large-scale environment is still an open issue. For this work we measured the large-scale galaxies’ density around RGs present in the revised Third Cambridge Catalog of radio sources (3CR) with 0.02 < z < 0.3. The goal is to determine whether the accretion mode and morphology of RGs are related to the richness of the environment. We considered RGs at 0.05 < z < 0.3 for a comparison between optical spectroscopic classes, and those within 0.02 < z < 0.1 to study the differences between the radio morphological types. Photometric data from the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS) survey were used to search for “red sequences” within an area of 500 kpc of radius around each RG. We find that (1) RGs span over a large range of local galaxies’ density, from isolated sources to those in rich environments, (2) the richness distributions of the various classes are not statistically different, and (3) the radio luminosity is not connected with the source environment. Our results suggest that the RG properties are independent of the local galaxies density, which is in agreement with some previous analyses, but contrasting with other studies. We discuss the possible origin of this discrepancy. An analysis of a larger sample is needed to put out results on a stronger statistical basis.
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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".