Identifying active galactic nuclei via brightness temperature with sub-arcsecond international LOFAR telescope observations
Bibliographic record
Abstract
ABSTRACT Identifying active galactic nuclei (AGNs) and isolating their contribution to a galaxy’s energy budget is crucial for studying the co-evolution of AGNs and their host galaxies. Brightness temperature (Tb) measurements from high-resolution radio observations at GHz frequencies are widely used to identify AGNs. Here, we investigate using new sub-arcsecond imaging at 144 MHz with the International LOFAR Telescope to identify AGNs using Tb in the Lockman Hole field. We use ancillary data to validate the 940 AGN identifications, finding 83 percent of sources have AGN classifications from SED fitting and/or photometric identifications, yielding 160 new AGN identifications. Considering the multiwavelength classifications, brightness temperature criteria select over half of radio-excess sources, 32 percent of sources classified as radio-quiet AGNs, and 20 percent of sources classified as star-forming galaxies. Infrared colour–colour plots and comparison with what we would expect to detect based on peak brightness in 6 arcsec LOFAR maps imply that the star-forming galaxies and sources at low flux densities have a mixture of star-formation and AGN activity. We separate the radio emission from star-formation and AGN in unresolved, Tb-identified AGNs with no significant radio excess and find the AGN comprises 0.49 ± 0.16 of the radio luminosity. Overall, the non-radio excess AGNs show evidence for having a variety of different radio emission mechanisms, which can provide different pathways for AGNs and galaxy co-evolution. This validation of AGN identification using brightness temperature at low frequencies opens the possibility for securely selecting AGN samples where ancillary data are inadequate.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".