The nature of HERGs and LERGs in LoTSS DR2 − a morphological perspective
Bibliographic record
Abstract
ABSTRACT We present the largest visually selected sample of extended ($>$60 arcsec) radio-loud active galactic nuclei (RLAGN) to date, based on the LOw-Frequency Array Two-Metre Sky Survey second data release (LoTSS DR2). From the broader LoTSS DR2 data set with spectroscopic classifications, we construct a subsample of 2828 RLAGN with radio luminosities greater than $10^{23}~\mathrm{W~Hz^{-1}}$ at $z<0.57$. These RLAGN are further classified by optical emission-line properties into high-excitation and low-excitation radio galaxies, enabling a detailed emission-line analysis. Our subsample is also morphologically classified into Fanaroff–Riley centre- and edge-brightened sources, wide- and narrow-angle tail sources, head–tail sources, and relaxed double sources. For these classifications, we utilize data from the Very Large Array Sky Survey to assist with the classification, taking advantage of its 2.5 arcsec resolution which is sensitive to structures below 30 arcsec. This resolution allows us to identify compact cores and hotspots, facilitating the identification of remnant and restarted RLAGN candidates. We investigate the relationship between emission-line and radio properties in RLAGN, analysing mid-infrared data, host galaxy mass, and core prominence. These analyses uncover the complex relationship between these factors and the underlying accretion mechanisms. Our findings emphasize that no single property can fully constrain the accretion mode in RLAGN, highlighting the necessity of multidimensional approaches to reveal the processes driving RLAGN behaviour.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".